{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning the Car Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/Nicolas/Documents/Scraping/thecarconnection/full_specs.csv\", index_col=0, \n",
    "                 low_memory=False).transpose()\n",
    "df.index = df.index.str.replace(' Specs: ', ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbols = '!@#$%^&*()_+[]-–'\n",
    "letters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\n",
    "numbers = '0123456789'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Editing Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Detecting hybrid cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    34363\n",
       "1      971\n",
       "Name: Hybrid, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hybrid'] = df['Hybrid/Electric Components Years'].apply(lambda x: 1 if len(str(x))!=3 else 0)\n",
    "df['Hybrid'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Detecting hybrid cars (2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    34230\n",
       "1     1104\n",
       "Name: Hybrid Engine, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hybrid Engine'] = df['Engine Type'].apply(lambda x: 1 if 'electric' in str.lower(str(x)) else 0)\n",
    "df['Hybrid Engine'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replace 'NA' and 'TBD' with `np.nan`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(\"- TBD –\", 'NA')\n",
    "df = df.replace(\"- TBD -\", 'NA')\n",
    "df = df.replace(\"NA\", np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Forbidden characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns=lambda x: x.split(\" (ft\")[0])\n",
    "df['EPA Fuel Economy Est - City (MPG)'] = df['EPA Fuel Economy Est - City (MPG)'].str.replace(r\"\\(.*\\)\",\"\")\n",
    "df['Passenger Volume'] = df['Passenger Volume'].str.replace(r\"\\(.*\\)\",\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `MSRP`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.MSRP = df.MSRP.str.replace(\"$\", \"\").str.replace(\",\", \"\").astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Make`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ford', 'Chevrolet', 'Toyota', 'Nissan', 'GMC', 'Honda', 'Volkswagen',\n",
       "       'Subaru', 'BMW', 'Audi', 'Ram', 'Hyundai', 'Mercedes-Benz', 'Jeep',\n",
       "       'Mazda', 'Kia', 'Porsche', 'Cadillac', 'Volvo', 'Dodge', 'Jaguar',\n",
       "       'Buick', 'Acura', 'Land Rover', 'Lincoln', 'Mitsubishi', 'Lexus',\n",
       "       'Infiniti', 'Chrysler', 'Fiat', 'Maserati', 'Mini', 'Bentley', 'Tesla',\n",
       "       'Rolls-Royce', 'Smart', 'Aston Martin', 'Alfa Romeo', 'Lamborghini',\n",
       "       'Genesis', 'Lotus', 'McLaren', 'Ferrari'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Make'] = df['Make'].str.replace('INFINITI', 'Infiniti').replace('smart', 'Smart')\n",
    "df.index = df.index.str.replace('INFINITI', 'Infiniti').str.replace('smart', 'Smart')\n",
    "df['Make'] = df['Make'].str.replace('FIAT', 'Fiat').replace('MINI', 'Mini')\n",
    "df['Make'].value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Basic miles`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36000.0     21525\n",
       "50000.0      7997\n",
       "60000.0      2633\n",
       "150000.0      344\n",
       "100000.0      295\n",
       "24000.0       255\n",
       "72000.0       162\n",
       "42000.0         5\n",
       "Name: Basic Miles/km, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Basic Miles/km'] = df['Basic Miles/km'].str.replace(\",\", \"\").str.replace(\"Unlimited\", \"150000\")\n",
    "df['Basic Miles/km'] = df['Basic Miles/km'].str.replace(\"49999\", \"50000\")\n",
    "df['Basic Miles/km'] = df['Basic Miles/km'].astype(float)\n",
    "df['Basic Miles/km'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Drivetrain miles` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000.0     14495\n",
       "100000.0     6800\n",
       "50000.0      5521\n",
       "36000.0      2973\n",
       "70000.0      1871\n",
       "150000.0      476\n",
       "24000.0       227\n",
       "72000.0       162\n",
       "80000.0        10\n",
       "120000.0        7\n",
       "125000.0        1\n",
       "Name: Drivetrain Miles/km, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drivetrain Miles/km'] = df['Drivetrain Miles/km'].str.replace(\",\", \"\").str.replace(\"Unlimited\", \"150000\")\n",
    "df['Drivetrain Miles/km'] = df['Drivetrain Miles/km'].astype(float)\n",
    "df['Drivetrain Miles/km'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Roadside assistance`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000.0    10644\n",
       "36000.0      7841\n",
       "60000.0      5785\n",
       "50000.0      2798\n",
       "70000.0       767\n",
       "25000.0       401\n",
       "24000.0        35\n",
       "Name: Roadside Assistance Miles/km, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Roadside Assistance Miles/km'] = df['Roadside Assistance Miles/km'].str.replace(\",\", \"\")\n",
    "df['Roadside Assistance Miles/km'] = df['Roadside Assistance Miles/km'].str.replace(\"Unlimited\", \"100000\")\n",
    "df['Roadside Assistance Miles/km'] = df['Roadside Assistance Miles/km'].astype(float)\n",
    "df['Roadside Assistance Miles/km'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Turning diameter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Turning Diameter - Curb to Curb'] = df['Turning Diameter - Curb to Curb'].str.strip('-')\n",
    "df['Turning Diameter - Curb to Curb'] = df['Turning Diameter - Curb to Curb'].str.split().str.get(0)\n",
    "df['Turning Diameter - Curb to Curb'] = df['Turning Diameter - Curb to Curb'].str.replace('TBD', \n",
    "                                                            '').replace(r'^\\s*$', np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a331272a58>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Turning Diameter - Curb to Curb'].dropna(), color='cadetblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full-size Cargo Van           22\n",
       "Crew Cab Pickup - Long Bed    21\n",
       "Specialty Vehicle             20\n",
       "Mini-van, Cargo                3\n",
       "Name: Body Style, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Turning Diameter - Curb to Curb'] > 60]['Body Style'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Reverse ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Reverse Ratio (:1)'] = df['Reverse Ratio (:1)'].str.split('/').str.get(0).str.strip('-TBD-')\n",
    "df['Reverse Ratio (:1)'] = df['Reverse Ratio (:1)'].str.split('-').str.get(0).str.strip('Variable')\n",
    "df['Reverse Ratio (:1)'] = df['Reverse Ratio (:1)'].str.split().str.get(0).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34af89f28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Reverse Ratio (:1)'].dropna(), color='forestgreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Reverse Ratio (:1)'] = df['Reverse Ratio (:1)'].clip(upper=10, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Number of gears`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0     12541\n",
       "5.0      8336\n",
       "4.0      5168\n",
       "8.0      3418\n",
       "7.0      1579\n",
       "9.0       554\n",
       "10.0      190\n",
       "1.0       106\n",
       "3.0        21\n",
       "Name: Gears, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transmission'] = df['Transmission'].str.lower()\n",
    "df['Gears'] = df['Transmission'].str.split(\"-speed\", expand=True, n = 1)[0].str[-2:].str.strip()\n",
    "df['Gears'] = df['Gears'].str.strip(letters).str.strip('/').str.strip(',').replace(r'^\\s*$', np.nan, regex=True)\n",
    "df['Gears'] = df['Gears'].astype(float)\n",
    "df['Gears'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Horsepower`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Net Horsepower'] = df['SAE Net Horsepower @ RPM'].str.split(\"@\",expand=True)[0]\n",
    "df['Net Horsepower'] = df['Net Horsepower'].str.replace('\\–|\\(|T|\\-', '').str.replace('BD', '')\n",
    "df['Net Horsepower'] = df['Net Horsepower'].replace(\" \", 'NA')\n",
    "df['Net Horsepower'] = df['Net Horsepower'].str[:3]\n",
    "df['Net Horsepower'] = df['Net Horsepower'].replace(r'^\\s*$', np.nan, regex=True)\n",
    "df['Net Horsepower'] = df['Net Horsepower'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Net Horsepower RPM`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Net Horsepower RPM'] = df['SAE Net Horsepower @ RPM'].str.split(\"@\",expand=True)[1].str.split('-').str.get(0)\n",
    "df['Net Horsepower RPM'] = df['Net Horsepower RPM'].str.replace(\"- TBD -\", \"NA\").replace(\"NA\", np.nan)\n",
    "df['Net Horsepower RPM'] = df['Net Horsepower RPM'].str.split('/').str.get(0).str.replace('\\(est\\)', '').str.strip(symbols)\n",
    "df['Net Horsepower RPM'] = df['Net Horsepower RPM'].str[:4]\n",
    "df['Net Horsepower RPM'] = df['Net Horsepower RPM'].replace(r'^\\s*$', np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Net Torque`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Net Torque'] = df['SAE Net Torque @ RPM']\n",
    "df['Net Torque'] = df['Net Torque'].str.split(\"@\", expand=True)\n",
    "df['Net Torque'] = df['Net Torque'].str.split(\" \", expand=True)[0]\n",
    "df['Net Torque'] = df['Net Torque'].str.replace('3350', '335').str.strip(symbols).replace(r'^\\s*$', np.nan, regex=True)\n",
    "df['Net Torque'] = df['Net Torque'].astype(float)\n",
    "# df[df['Net Torque'].apply(lambda x: len(str(x)) >= 5)]['Net Torque']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Net Torque RPM`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Net Torque RPM'] = df['SAE Net Torque @ RPM'].str.split().str.get(2)\n",
    "df['Net Torque RPM'] = df['Net Torque RPM'].str.split('-').str.get(0)\n",
    "df['Net Torque RPM'] = df['Net Torque RPM'].str.split('/').str.get(0)\n",
    "df['Net Torque RPM'] = df['Net Torque RPM'].replace(r'^\\s*$', np.nan, regex=True)\n",
    "df['Net Torque RPM'] = df['Net Torque RPM'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Cylinders`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0     11750\n",
       "6.0     10970\n",
       "8.0      9316\n",
       "5.0       531\n",
       "12.0      310\n",
       "3.0       136\n",
       "10.0       87\n",
       "Name: Cylinders, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cylinders'] = df['Engine Type'].str.strip(letters).str.strip(symbols).str.strip(letters).str.strip(symbols)\n",
    "df['Cylinders'] = df['Cylinders'].str.strip().str.split().str.get(-1)\n",
    "df['Cylinders'] = df['Cylinders'].str.replace(\"-\", \"\").str.replace(\"/\", \"\")\n",
    "df['Cylinders'] = df['Cylinders'].str.lstrip(letters)\n",
    "df['Cylinders'] = df['Cylinders'].replace(r'^\\s*$', np.nan, regex=True).replace('4Cyl', '4')\n",
    "df['Cylinders'] = df['Cylinders'].astype(float)\n",
    "df['Cylinders'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Engine Configuration`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V       19155\n",
       "I       11742\n",
       "FLAT      966\n",
       "H         431\n",
       "W          83\n",
       "Name: Engine Configuration, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = ['turbo', 'supercharger', 'regular', 'unleaded', 'premium', 'gas', 'electric', 'turbocharged', 'flexible',\n",
    "          'intercooled', 'twin', 'unleaded', 'charged', 'ethanol', 'natural', 'high pressure', 'low pressure',\n",
    "          'ecotec', 'cyl', 'diesel', 'compressed', 'super', 'vortec', '4', '6', '8', '5', '(']\n",
    "df['Engine Configuration'] = df['Engine Type'].str.lower() \n",
    "df['Engine Configuration'] = df['Engine Configuration'].str.strip(numbers).str.lower()\n",
    "df['Engine Configuration'] = df['Engine Configuration'].str.replace('-', \" \").str.replace('/', \" \")\n",
    "df['Engine Configuration'] = df['Engine Configuration'].str.strip(symbols).str.rstrip(numbers)\n",
    "\n",
    "for i in config:\n",
    "    df['Engine Configuration'] = df['Engine Configuration'].str.replace(i, \" \")\n",
    "df['Engine Configuration'] = df['Engine Configuration'].str.strip().str[-1]\n",
    "df['Engine Configuration'] = df['Engine Configuration'].str.upper().str.replace('T', 'FLAT').replace('L', np.nan)\n",
    "df['Engine Configuration'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Displacement (L)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    4334\n",
       "3.5    2419\n",
       "2.5    2410\n",
       "3.0    2179\n",
       "3.6    1962\n",
       "Name: Displacement (L), dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Displacement (L)'] = df['Displacement'].str.split(\"/\", expand=True)[0].str[:3]\n",
    "df['Displacement (L)'] = df['Displacement (L)'].str.replace('39.', '3.9')\n",
    "df['Displacement (L)'] = df['Displacement (L)'].str.strip(symbols).str.strip(letters).replace(r'^\\s*$',\n",
    "                                                                        np.nan, regex=True).astype(float)\n",
    "df['Displacement (L)'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Displacement (cc)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Displacement (cc)'] = df['Displacement'].str.split(\"/\", expand=True)[1]\n",
    "df['Displacement (cc)'] = df['Displacement (cc)'].str.replace('- TBD -', 'NA')\n",
    "df['Displacement (cc)'] = df['Displacement (cc)'].str.replace('- TBD –', 'NA')\n",
    "df['Displacement (cc)'] = df['Displacement (cc)'].replace('NA', np.nan).str.strip(letters)\n",
    "df['Displacement (cc)'] = df['Displacement (cc)'].replace(r'^\\s*$', np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Rear Tire Width`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245.0    5851\n",
       "235.0    4869\n",
       "225.0    4265\n",
       "265.0    3542\n",
       "215.0    2916\n",
       "205.0    2715\n",
       "255.0    2500\n",
       "275.0    2497\n",
       "Name: Rear Tire Width, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Rear Tire Width\"] = df[\"Rear Tire Size\"].str.split(\"/\").str.get(0).str[-3:].str.strip()\n",
    "df[\"Rear Tire Width\"] = df[\"Rear Tire Width\"].replace('R20', np.nan).replace('R18', np.nan)\n",
    "df[\"Rear Tire Width\"] = df[\"Rear Tire Width\"].replace('D -', np.nan).replace('R15', np.nan).replace('R15', np.nan)\n",
    "df[\"Rear Tire Width\"] = df[\"Rear Tire Width\"].replace('18\"', np.nan).replace('60A', np.nan)\n",
    "df[\"Rear Tire Width\"] = df[\"Rear Tire Width\"].astype(float)\n",
    "df[\"Rear Tire Width\"].value_counts().head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front Tire Width`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245.0    6472\n",
       "235.0    5203\n",
       "225.0    4634\n",
       "265.0    3522\n",
       "215.0    2947\n",
       "205.0    2795\n",
       "255.0    2702\n",
       "275.0    2142\n",
       "Name: Front Tire Width, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Front Tire Width\"] = df[\"Front Tire Size\"].str.split(\"/\").str.get(0).str[-3:].str.strip()\n",
    "df[\"Front Tire Width\"] = df[\"Front Tire Width\"].replace('R20', 'NA').str.strip(letters).str.strip(symbols)\n",
    "df[\"Front Tire Width\"] = df[\"Front Tire Width\"].replace('18\"', 'NA').replace('R15', '').replace('D -', '')\n",
    "df[\"Front Tire Width\"] = df[\"Front Tire Width\"].replace(\"NA\", np.nan).replace(\"60\", np.nan).replace(\"15\", np.nan)\n",
    "df[\"Front Tire Width\"] = df[\"Front Tire Width\"].replace(r'^\\s*$', np.nan, regex=True).astype(float)\n",
    "df[\"Front Tire Width\"].value_counts().head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Rear Wheel Size`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.0    8952\n",
       "18.0    7500\n",
       "16.0    7188\n",
       "15.0    3268\n",
       "19.0    2581\n",
       "14.0     495\n",
       "13.0      84\n",
       "Name: Rear Wheel Size, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Rear Wheel Size\"] = df[\"Rear Wheel Size (in)\"].str[:2].str.strip('-').str.strip('P2')\n",
    "df[\"Rear Wheel Size\"] = df[\"Rear Wheel Size\"].replace(r'^\\s*$', np.nan, regex=True)\n",
    "df[\"Rear Wheel Size\"] = df[\"Rear Wheel Size\"].replace('1', np.nan).replace('0', np.nan).astype(float)\n",
    "df[\"Rear Wheel Size\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front Wheel Size`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.0    8962\n",
       "18.0    7614\n",
       "16.0    7208\n",
       "15.0    3272\n",
       "19.0    2606\n",
       "14.0     495\n",
       "13.0      84\n",
       "Name: Front Wheel Size, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Front Wheel Size\"] = df[\"Front Wheel Size (in)\"].str[:2].str.strip('-').str.strip('P2')\n",
    "df[\"Front Wheel Size\"] = df[\"Front Wheel Size\"].replace('0', np.nan).replace('1', np.nan)\n",
    "df[\"Front Wheel Size\"] = df[\"Front Wheel Size\"].replace(r'^\\s*$', np.nan, regex=True).astype(float)\n",
    "df[\"Front Wheel Size\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Tire Rating`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R    16516\n",
       "H     6054\n",
       "V     3028\n",
       "S     2754\n",
       "T     1516\n",
       "Y     1289\n",
       "Z     1254\n",
       "W      903\n",
       "Name: Tire Rating, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Tire Rating\"] = df[\"Front Tire Size\"].str.split(\"/\").str.get(-1).str.strip(numbers).str[0].str.upper()\n",
    "df[\"Tire Rating\"] = df[\"Tire Rating\"].replace(r'^\\s*$', np.nan, regex=True).replace('-', np.nan)\n",
    "df[\"Tire Rating\"] = df[\"Tire Rating\"].replace('\"', np.nan).replace('P', np.nan).replace('X', np.nan)\n",
    "df[\"Tire Rating\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Tire Width Ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Tire Width Ratio\"] = df[\"Rear Tire Width\"]/df[\"Front Tire Width\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Wheel Size Ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Wheel Size Ratio\"] = df[\"Rear Wheel Size\"] / df[\"Front Wheel Size\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Tire Ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Tire Ratio\"] = df[\"Front Tire Size\"].str.split(\"/\").str.get(1).str[0]\n",
    "df[\"Tire Ratio\"] = df[\"Tire Ratio\"].replace('Y', 'NA').replace('NA', np.nan).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Year`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018.0    2806\n",
       "2019.0    2722\n",
       "2017.0    2624\n",
       "2016.0    2233\n",
       "2015.0    2118\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Year\"] = df.index.str[:4].astype(float)\n",
    "df[\"Year\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Drivetrain`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rear Wheel Drive     10035\n",
       "Front Wheel Drive     9962\n",
       "Four Wheel Drive      6939\n",
       "All Wheel Drive       6682\n",
       "Name: Drivetrain, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drivetrain'] = df['Drivetrain'].str.replace('-', '').str.title()\n",
    "df['Drivetrain'] = df['Drivetrain'].str.replace('4', 'Four').str.replace('2', 'Four')\n",
    "df['Drivetrain'] = df['Drivetrain'].str.replace('FourWheel', 'Four Wheel').str.replace('FrontWheel', 'Front Wheel')\n",
    "df['Drivetrain'] = df['Drivetrain'].str.replace('Allwheel', 'All Wheel').str.replace('Frontwheel', 'Front Wheel')\n",
    "df['Drivetrain'] = df['Drivetrain'].str.replace('Fourwheel', 'Four Wheel').str.replace('Rearwheel', 'Rear Wheel')\n",
    "df['Drivetrain'] = df['Drivetrain'].str.replace('Awd', 'All Wheel Drive').str.replace('FourWd', 'Four Wheel Drive')\n",
    "df['Drivetrain'] = df['Drivetrain'].str.replace('Rwd', 'Rear Wheel Drive')\n",
    "df['Drivetrain'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Sixth Gear Ratio (:1)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sixth Gear Ratio (:1)'] = df['Sixth Gear Ratio (:1)'].str.strip(letters).str.strip(symbols)\n",
    "df['Sixth Gear Ratio (:1)'] = df['Sixth Gear Ratio (:1)'].str.strip(' TBD ').replace(r'^\\s*$', np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Country of origin`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ford', 'Chevrolet', 'Toyota', 'Nissan', 'GMC', 'Honda', 'Volkswagen',\n",
       "       'Subaru', 'BMW', 'Audi', 'Ram', 'Hyundai', 'Mercedes-Benz', 'Jeep',\n",
       "       'Mazda', 'Kia', 'Porsche', 'Cadillac', 'Volvo', 'Dodge', 'Jaguar',\n",
       "       'Buick', 'Acura', 'Land Rover', 'Lincoln', 'Mitsubishi', 'Lexus',\n",
       "       'Infiniti', 'Chrysler', 'Fiat', 'Maserati', 'Mini', 'Bentley', 'Tesla',\n",
       "       'Rolls-Royce', 'Smart', 'Aston Martin', 'Alfa Romeo', 'Lamborghini',\n",
       "       'Genesis', 'Lotus', 'McLaren', 'Ferrari'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Make'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Country'] = df['Make']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Country'] = df['Country'].replace(['Ford', 'Chevrolet', 'GMC', 'Ram', 'Jeep', 'Cadillac', 'Dodge',\n",
    "                                      'Buick', 'Lincoln', 'Chrysler', 'Tesla'], 'USA')\n",
    "df['Country'] = df['Country'].replace(['Jaguar', 'Land Rover', 'Bentley', 'Rolls-Royce', \n",
    "                                       'Aston Martin', 'Lotus', 'McLaren', 'Mini'], 'UK')\n",
    "df['Country'] = df['Country'].replace(['Toyota', 'Nissan', 'Honda', 'Subaru', 'Mazda', 'Acura', \n",
    "                                       'Mitsubishi', 'Lexus', 'Infiniti'], 'Japan')\n",
    "df['Country'] = df['Country'].replace(['Volkswagen', 'BMW', 'Audi', 'Mercedes-Benz', 'Porsche', 'Smart'], 'Germany')\n",
    "df['Country'] = df['Country'].replace(['Hyundai', 'Kia', 'Genesis'], 'Korea')\n",
    "df['Country'] = df['Country'].replace(['Volvo'], 'Sweden')\n",
    "df['Country'] = df['Country'].replace(['Fiat', 'Maserati', 'Alfa Romeo', 'Lamborghini', 'Ferrari'], 'Italy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA        14072\n",
       "Japan      10942\n",
       "Germany     6164\n",
       "Korea       1964\n",
       "UK          1221\n",
       "Sweden       572\n",
       "Italy        399\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Country Code`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Country Code'] = df['Country'].astype(\"category\").cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    14072\n",
       "2    10942\n",
       "0     6164\n",
       "3     1964\n",
       "5     1221\n",
       "4      572\n",
       "1      399\n",
       "Name: Country Code, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country Code'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Redundant Specs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_delete = ['Gas Mileage', 'Engine', 'Engine Type', 'SAE Net Horsepower @ RPM', 'SAE Net Torque @ RPM',\n",
    "                  'Displacement', 'Trans Description Cont.', 'Rear Tire Size', 'Front Tire Size', 'Rear Wheel Size (in)',\n",
    "                  'Front Wheel Size (in)', 'Transmission', 'EPA Class', 'Brake ABS System', 'Disc - Front (Yes or   )',\n",
    "                  'Brake Type', 'Disc - Rear (Yes or   )', 'Spare Tire Size', 'Spare Wheel Size (in)',\n",
    "                   'Spare Wheel Material', 'Trans Type']\n",
    "df = df.drop(specs_to_delete, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identifying columns with NaN totalling more than `threshold` elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 9e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_delete = df.columns[df.isna().sum() >= threshold].tolist()\n",
    "col_to_delete.remove('EPA Classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Base Curb Weight (lbs)',\n",
       " 'Second Hip Room (in)',\n",
       " 'Front Hip Room (in)',\n",
       " 'Passenger Volume',\n",
       " 'Track Width, Rear (in)',\n",
       " 'Min Ground Clearance (in)',\n",
       " 'Track Width, Front (in)',\n",
       " 'Cargo Volume to Seat 3',\n",
       " 'Cargo Volume to Seat 1',\n",
       " 'Cargo Volume to Seat 2',\n",
       " 'Fuel Economy Est-Combined (MPG)',\n",
       " 'Engine Order Code',\n",
       " 'Trans Description Cont. Again',\n",
       " 'Fifth Gear Ratio (:1)',\n",
       " 'Final Drive Axle Ratio (:1)',\n",
       " 'Sixth Gear Ratio (:1)',\n",
       " 'Trans Order Code',\n",
       " 'Reverse Ratio (:1)',\n",
       " 'Rear Drum Diam x Width (in)',\n",
       " 'Drum - Rear (Yes or   )',\n",
       " 'Spare Tire Order Code',\n",
       " 'Front Tire Order Code',\n",
       " 'Rear Tire Order Code',\n",
       " 'Suspension Type - Front (Cont.)',\n",
       " 'Suspension Type - Rear (Cont.)',\n",
       " 'Cold Cranking Amps @ 0° F (Primary)',\n",
       " 'Maximum Alternator Capacity (amps)',\n",
       " 'Maximum Trailering Capacity (lbs)',\n",
       " 'Wt Distributing Hitch - Max Trailer Wt. (lbs)',\n",
       " 'Dead Weight Hitch - Max Trailer Wt. (lbs)']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_to_delete[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(col_to_delete, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv(r'C:/Users/Nicolas/Documents/Scraping/thecarconnection/df_cleaning_all_may.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting remaining columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSRP', 'Make', 'Model', 'Style Name', 'Drivetrain',\n",
       "       'Passenger Capacity', 'Passenger Doors', 'Body Style',\n",
       "       'EPA Classification', 'Front Shoulder Room (in)',\n",
       "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
       "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
       "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
       "       'Fuel Tank Capacity, Approx (gal)', 'EPA Fuel Economy Est - Hwy (MPG)',\n",
       "       'EPA Fuel Economy Est - City (MPG)', 'Fuel System',\n",
       "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
       "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
       "       'Front Brake Rotor Diam x Thickness (in)',\n",
       "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
       "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
       "       'Front Wheel Material', 'Suspension Type - Front',\n",
       "       'Suspension Type - Rear', 'Air Bag-Frontal-Driver',\n",
       "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
       "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
       "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
       "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
       "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
       "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
       "       'Stability Control', 'Other Features', 'Basic Miles/km', 'Basic Years',\n",
       "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
       "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
       "       'Roadside Assistance Years', 'Hybrid', 'Hybrid Engine', 'Gears',\n",
       "       'Net Horsepower', 'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM',\n",
       "       'Cylinders', 'Engine Configuration', 'Displacement (L)',\n",
       "       'Displacement (cc)', 'Rear Tire Width', 'Front Tire Width',\n",
       "       'Rear Wheel Size', 'Front Wheel Size', 'Tire Rating',\n",
       "       'Tire Width Ratio', 'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country',\n",
       "       'Country Code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSRP                                          67\n",
       "Make                                           0\n",
       "Model                                          0\n",
       "Style Name                                     0\n",
       "Drivetrain                                  1716\n",
       "Passenger Capacity                             0\n",
       "Passenger Doors                                0\n",
       "Body Style                                  1716\n",
       "EPA Classification                         13445\n",
       "Front Shoulder Room (in)                    3312\n",
       "Front Head Room (in)                        3258\n",
       "Second Leg Room (in)                        6777\n",
       "Front Leg Room (in)                         3265\n",
       "Second Shoulder Room (in)                   6851\n",
       "Second Head Room (in)                       6757\n",
       "Height, Overall (in)                        2177\n",
       "Wheelbase (in)                              2019\n",
       "Width, Max w/o mirrors (in)                 2117\n",
       "Fuel Tank Capacity, Approx (gal)            2217\n",
       "EPA Fuel Economy Est - Hwy (MPG)            5774\n",
       "EPA Fuel Economy Est - City (MPG)           5770\n",
       "Fuel System                                 2875\n",
       "Third Gear Ratio (:1)                       5292\n",
       "First Gear Ratio (:1)                       3863\n",
       "Fourth Gear Ratio (:1)                      5303\n",
       "Second Gear Ratio (:1)                      5287\n",
       "Front Brake Rotor Diam x Thickness (in)     4439\n",
       "Rear Brake Rotor Diam x Thickness (in)      8155\n",
       "Steering Type                               2024\n",
       "Turning Diameter - Curb to Curb             3549\n",
       "                                           ...  \n",
       "Basic Miles/km                              2118\n",
       "Basic Years                                 2118\n",
       "Corrosion Miles/km                          2946\n",
       "Corrosion Years                             2906\n",
       "Drivetrain Miles/km                         2791\n",
       "Drivetrain Years                            2791\n",
       "Roadside Assistance Miles/km                7063\n",
       "Roadside Assistance Years                   7060\n",
       "Hybrid                                         0\n",
       "Hybrid Engine                                  0\n",
       "Gears                                       3421\n",
       "Net Horsepower                              2010\n",
       "Net Horsepower RPM                          2308\n",
       "Net Torque                                  2073\n",
       "Net Torque RPM                              2431\n",
       "Cylinders                                   2234\n",
       "Engine Configuration                        2957\n",
       "Displacement (L)                            2193\n",
       "Displacement (cc)                           3177\n",
       "Rear Tire Width                             2006\n",
       "Front Tire Width                            2005\n",
       "Rear Wheel Size                             5266\n",
       "Front Wheel Size                            5093\n",
       "Tire Rating                                 2020\n",
       "Tire Width Ratio                            2009\n",
       "Wheel Size Ratio                            5266\n",
       "Tire Ratio                                  2012\n",
       "Year                                           0\n",
       "Country                                        0\n",
       "Country Code                                   0\n",
       "Length: 83, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `MSRP`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'MSRP Distribution (clipped at 100,000$)')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BThaRFhHpxHWr3T+2tyBZQdQPYRMEWxpxUbPr3bMJAcaYHcGoiUZV1wBXAA8CTwK3q+pjInKPiBzid1sAXCsiK4E24AYfvxA3S+1Z4Ai2DOgvBN4vIiv8Pp/38e8AjT5+Ay5pAfwPYI6IPI1LRpeo6iuq+hhuxtvjwMPAQl/e7UYQ9ROHjVvFYks0xpgdSBDHYxufqCNvA15KdoxmHQ2/P5VwYD29e/3zUDyccwytDxxAzzuuoHevy8ZY7GRNxT7jSlndpiar2/alaIzm7cDLFR2TZIEMBFHWdZ0VCzNEqXaCwTfKH2SMMXXEEk3Cgnw/cWmiAeLMDEJLNMaYHYAlmqRF2W3GaACizAxr0RhjdgiWaBLmZp1tm2jizE6EgxsnoUTGGFNblmgS5madbdt1Zi0aY8yOwhJNkuLYd53ZGI0xZsdliSZJUR8BcdmusygzgyD3hktGxhhTxyzRJCjI9QCUnQwQZ3YiiPMEuU21LpYxxtSUJZok5X2iSZUfowFcq8YYY+qYJZoEBTn30LNtbtgE4rRLNOGAzTwzxtQ3SzRJGqHrLGpwDwK1Fo0xpt5ZoklQUOg6G6lFYzPPjDF1zhJNkvIjtGgKYzSWaIwxdc4STYJGHKPJFFo0NkZjjKlvlTzK2YzTcNOb06mIlvzrxKlWGgZWQ7wOgCjVTjYa2yNSjTFme2eJJkn5HmICCBu2Cgf5XqI1D7sEtOlZojXugaXhrh+BwBKNMaa+WNdZgoJct0syQfm3OU61DE0YMMaYemWJJkn5nrIzzgpcoumtYYGMMab2LNEkKMj1lJ1xVhCHrWCJxhhT5yzRJCnfU3bGWYG1aIwxOwJLNAkKct0jt2hSrS7R2ArOxpg6ZokmQUElYzREEGVrWCpjjKktSzRJyvVAmZWbC+JUC4B1nxlj6lpF99GIyJnAlUAGuE5VbyzZPg+4BegAlgIXqGpORPYAFgM7AwosUNVuEZkO3AbsBWwATlPV10SkAbgVOAToA85U1ZUi0gZ8H9gXCICrVfVOf+0XgeKHunxUVV8dx3tRda5FM3LX2dB+zKxVsYwxpqZGbdGIyK7A1cB8YB5wvojsX7LbYuAiVd0HlwjO8/GbgJtUdV9gObDQx78GLFPV/YCbget9/GKgx8cvARb5+OXAKlU9CDgGuEZEZovITGBAVecV/bddJBkARpl1hrVojDE7gEq6zo4FHlDVjaraA9wFnFLYKCJ7As2q+qgPLQJOFZEMcKTffyjufz4R16IBuAM43u8/FFfVpUCnbxU9BNzg4+uBjcAuwKFAICKPiMgTIlI4/+SL+gniwVFnnYElGmNMfauk62wusK7o9TrgsFG27wbMAjapaq4kvtUxvottE9A53LlU9deFgIicBjQCK4C3A78ELgNmA8tE5BlVfa6CegEwc+b4lnzp7GwfeYdsPwCNzW00tpckm0yahvYmaHLPpGnO9EN7E7Q20to6ynlrYNS6TWFWt6nJ6ja1VZJoQqB4/m0ARBVsL41TdFxQEh/umK2u5Vss1wMf8QnsJ/4/gJdF5G7gOKDiRNPV1U0UjW16cWdnOxs2bB5xn7B3LTOBvsEUuc1bzypr7MjRvzkLcYp2oL9nEwObs4Qd/fT2jnzepFVSt6nK6jY1Wd22L2EYjPkLeiVdZ6uBOUWvdwHWVrB9PTBNRFI+PqfouDV+P0QkDbQDXSNdS0S+AHwbOE5Vn/Kxk0TkkKL9A2CwgjolLsgXHhEwwhhNkCIOGwkiW+/MGFO/Kkk09wHHiEiniLQAJwP3Fjaq6itAVkQO96GzgSWqOggsA0738XOAJf7ne/xr/PZlfv+huIjMB7KqukpEPgH8I3C4qj5dVLa3AVeJSCgis4GPAb+ouPYJKjyLZqT7aKDopk1jjKlTo3adqeoaEbkCeBBoAG5R1cdE5B7gy6q6HFgA3CwiHcAT+IF74ELgByJyJbAKOMPHFwKLRGQF8KY/HuA7wPd8vB+XtAC+AjQD/ykihaJ9DvgucBDwDC5pXuYT36QLIp88Sh4RUMpWcDbG1LuK7qNR1duB20tiJxT9/BRbTxAoxF8BjioT34hrfZTGs8C5ZeIHj1C880fYNmmCvBuXiYPMiPvZemfGmHpnKwMkJCgsKxOOkmhC6zozxtQ3SzRJiSpt0TRDvq8WJTLGmElhiSYhQSF5BKP0ToZNW1o/xhhThyzRJKSQPOLRus5SzW4FgSg34n7GGDNVWaJJyFArJRhl1lnY7Pe37jNjTH2yRJOUfJaYAILUiLvFhccIWKIxxtQpSzQJCaKsWxUgKF1tZ2tDLRqbEGCMqVOWaBISRH0jPvRsSKrQdWYTAowx9ckSTVLy/cSjrAoA1qIxxtQ/SzQJCaK+EZ9FU1AYo7HJAMaYemWJJiFDYzSjKLRo7KZNY0y9skSTlHyWODV6oiFsJCawMRpjTN2yRJMQ16KpYDJAELrVAaxFY4ypU5ZoEuLGaCpo0eBXB7AxGmNMnbJEk5Ag309caaIJmyBvXWfGmPpkiSYpld5Hg7VojDH1zRJNQoKo8hYNYbON0Rhj6pYlmoQE+TGM0YRN1qIxxtQtSzRJibJj6zqzMRpjTJ2yRJOQIMqOYTJAsxvTieOES2WMMbVniSYJUY4gzo1tejMx5HsSLpgxxtSeJZoEDI23VDwZwK93lutOqETGGDN5LNEkIeoHih5qNorYPyqA3KakSmSMMZMmXclOInImcCWQAa5T1RtLts8DbgE6gKXABaqaE5E9gMXAzoACC1S1W0SmA7cBewEbgNNU9TURaQBuBQ4B+oAzVXWliLQB3wf2BQLgalW901/7UuA8XNK8XFXvHv/bUR1DU5XHMkYDBLnNSRXJGGMmzagtGhHZFbgamA/MA84Xkf1LdlsMXKSq++ASwXk+fhNwk6ruCywHFvr414BlqrofcDNwvY9fDPT4+CXAIh+/HFilqgcBxwDXiMhsETkUOMuXaz7wLRHZaQz1T0RhgcyKJwMUHhVgicYYU4cq6To7FnhAVTeqag9wF3BKYaOI7Ak0q+qjPrQIOFVEMsCRfv+huP/5RFyLBuAO4Hi//1BcVZcCnb5V9BBwg4+vBzYCuwAnAHeratbHfwOcNIb6J6OwEnOlXWeFRwVYojHG1KFKEs1cYF3R63XAbhVsnwVsUtVcmeOGjvHbNwGdw51LVX+tqqsAROQ0oBFYUUHZJsVYu86GHuc8aGM0xpj6U8kYTQgU3+ARAFEF20vjFB0XlMSHO2ara4nIqbhuto/4MaDRyjaqmTPbxrL7kM7O9uE3RikAmlraaGooc29MJk1De9M2+zeGfSOft0a2hzIkxeo2NVndprZKEs1q4Iii17sAa0u2zymzfT0wTURSqpr3+xSOW+P3Wy0iaaAd6Co61wul1xKRLwBfAo5T1adHuLZWUKchXV3dRNHYbpTs7Gxnw4bhu7kaNnYxDegbTJHr3vaO/8aOHP2bt463BSkGe9/krRHOWwuj1W0qs7pNTVa37UsYBmP+gl5J19l9wDEi0ikiLcDJwL2Fjar6CpAVkcN96GxgiaoOAsuA0338HGCJ//ke/xq/fZnffyguIvOBrKquEpFPAP8IHF6UZPDnO1lEWkSkEzdR4P7Kq5+QwnIylTz4zIvDZgKb3myMqUOjtmhUdY2IXAE8CDQAt6jqYyJyD/BlVV0OLABuFpEO4An8wD1wIfADEbkSWAWc4eMLgUUisgJ40x8P8B3gez7ej0taAF8BmoH/FJFC0T7ny7EYeNzXZaGqrhnPG1FNW2adNVR+UKrZbtg0xtSliu6jUdXbgdtLYicU/fwUcFiZ414BjioT3wh8rEw8C5xbJn7wCGX7NvDtEStQY8EYZ52Bn3lmLRpjTB2ylQGS4GedVfw8Gty9NNaiMcbUI0s0CQj8EjQVT2/GxmiMMfXLEk0CtiyqOdaus6k1+8QYYyphiSYJUT9xkIKwoiEwx7rOjDF1yhJNAoJ835ZlZSoUhy0E+V6IBhMqlTHGTA5LNAkIouyYus2geGFNG6cxxtQXSzQJCKJsxc+iKdjyqIC3kiiSMcZMGks0Scj3EY+xRVNYWDO0Fo0xps5YokmA6zob6xhNoevMWjTGmPpiiSYBruus8ntoYMvjnG2MxhhTbyzRJCGfHcess8IzaaxFY4ypL5ZoEuC6zsbXogmt68wYU2cs0STAdZ2NrUVDaNObjTH1yRJNAoLxzDoLQuJUq00GMMbUHUs0SYj6x55ogDjdbi0aY0zdsUSTgCDqG9OzaIak222MxhhTdyzRJCAYx6wzsBaNMaY+WaKptjiGKDumh54NHZput+nNxpi6Y4mm2uIBAuKhJWXGJGNdZ8aY+mOJpsqCocc422QAY4wBSzTV5x/jPL5E0+GmN8dxtUtljDGTxhJNlRUe4zzmGzYB0m0EcQ4Kj4I2xpg6YImmyoK8a9GMdQkacF1nAKFNCDDG1JGKHmovImcCVwIZ4DpVvbFk+zzgFqADWApcoKo5EdkDWAzsDCiwQFW7RWQ6cBuwF7ABOE1VXxORBuBW4BCgDzhTVVcWXedA4E5VfVdR7EWgeGDjo6r66ljehGoaatGMa3pzhztHbhMwp5rFMsaYSTNqi0ZEdgWuBuYD84DzRWT/kt0WAxep6j5AAJzn4zcBN6nqvsByYKGPfw1Ypqr7ATcD1/v4xUCPj18CLCoqxznAvUBrUWwmMKCq84r+m7QkA0CUBRjzEzYBSLcBEOTerGaJjDFmUlXSdXYs8ICqblTVHuAu4JTCRhHZE2hW1Ud9aBFwqohkgCP9/kNx//OJuBYNwB3A8X7/obiqLgU6RWQPEZkGfBw4o6RshwKBiDwiIk+IyKlMsiDvEg3jnAwAtrCmMaa+VJJo5gLril6vA3arYPssYJOq5socN3SM374J6BzuXKr6lqqeDKwqKVsT8EtcMvwUcI2I7FdBnRKzpetsfNObwR4VYIypL5WM0YRA8XzbAIgq2F4ap+i4oCQ+3DGl19qKqv4E+Il/+bKI3A0cBzw33DGlZs5sq3TXrXR2tpff0OeqNmPWLMg0QnuZhJNJ01Au3jHL/a9pAIY7fw0MW7c6YHWbmqxuU1sliWY1cETR612AtSXb55TZvh6YJiIpVc37fQrHrfH7rRaRNNAOdBWd64VhrrUVETkJeE1Vl/tQAAxWUKchXV3dRNHY7lvp7Gxnw4bNZbc1vvEGHUDXWzmamvuJNme33acjR3+ZeNjaQCvQ/eZ6+oY5f9JGqttUZ3Wbmqxu25cwDMb8Bb2SrrP7gGNEpFNEWoCTcYPyAKjqK0BWRA73obOBJao6CCwDTvfxc4Al/ud7/Gv89mV+/6G4iMwHsqpa2l1W7G3AVSISishs4GPALyqoU2KCqDBGM477aMJm4iBl05uNMXVl1ESjqmuAK4AHgSeB21X1MRG5R0QO8bstAK4VkZVAG3CDj1+Im6X2LK5VdKWPLwTeLyIr/D6f9/HvAI0+fgMuaY3ku7hxnGeAh4DLfOKbNMFEZp0FAXF6mj38zBhTVyq6j0ZVbwduL4mdUPTzU8BhZY57BTiqTHwjrvVRGs8C545QjpdxrZjC6xxw/ug1qCE/68xNBugf8+FDy9AYY0ydsJUBqqww62w805sBovR0m95sjKkrlmiqLIj6iYMGCMb31sbpDpvebIypK5Zoqi3fN74FNb04M81aNMaYumKJpsqCKDuumzUL3BiNJRpjTP2wRFNlQZQd9/gMQJSeZo9zNsbUFUs0VRbks+Ob2uzF6Q7C/GaI81UslTHGTB5LNNUWZcf1iIACW1jTGFNvLNFUmes6G/tDzwqi9HR3Hks0xpg6YYmmyoKJzjobatHYOI0xpj5Yoqm2qH9is84y0wAIB61FY4ypD5ZoqiyI+iY068xaNMaYelPRWmemchOZdZZORQSpAQCaBleRjt0z4KJUO9lofM/NMcaYyWaJptomcMNmkO8l2vAYAHHXY0SxO0+460cgsERjjJmarOusyia8MoCfSBDk+6pVJGOMmVSWaKosyPfBBGadEaSIg4Ytq0AbY8wUZ4mmmuKIIB4gnsB9NOAfmmYtGmNMnbBEU02Fp2tOYGUAgDjVRpjvrkaJjDFm0lmiqaLCY5yZwFpngHuc8+CbVSiRMcZMPkspDXkQAAAZhElEQVQ0VRRs9Rjn8Ysy0wlylmiMMfXBEk01+QH8iSaaOD3NdZ3FuWqUyhhjJpUlmioKon6ACa11BhDbwprGmDpiiaaKhu59meCss8ivd2YPQDPG1ANLNFUUVGvWWdovrGnjNMaYOlDREjQiciZwJZABrlPVG0u2zwNuATqApcAFqpoTkT2AxcDOgAILVLVbRKYDtwF7ARuA01T1NRFpAG4FDgH6gDNVdWXRdQ4E7lTVdxXFLgXOwyXNy1X17nG8D9VRSDQTnHUWZXzXmc08M8bUgVFbNCKyK3A1MB+YB5wvIvuX7LYYuEhV9wEC3Ac/wE3ATaq6L7AcWOjjXwOWqep+wM3A9T5+MdDj45cAi4rKcQ5wL9BaFDsUOMuXaz7wLRHZqaKaJ6Aw62wiqze741uIgwyhreBsjKkDlXSdHQs8oKobVbUHuAs4pbBRRPYEmlX1UR9aBJwqIhngSL//UNz/fCKuRQNwB3C8338orqpLgU4R2UNEpgEfB84oKdsJwN2qmlXV9cBvgJMqqFMiqtV1RhC4e2ks0Rhj6kAliWYusK7o9Tpgtwq2zwI2qWquzHFDx/jtm4DO4c6lqm+p6snAqjGWrbaGus4mNhkA3IQAmwxgjKkHlYzRhEBc9DoAogq2l8YpOi4oiQ93TOm1xlq2Uc2cOb7l9zs727cNvumKMrOzE5rboWcjtJfpRsukaRgt3rQT9LxEe3sTtDbS2lrmegkpW7c6YXWbmqxuU1sliWY1cETR612AtSXb55TZvh6YJiIpVc37fQrHrfH7rRaRNNAOdBWd64VhrlWubKXX1grqNKSrq5soKs2HI+vsbGfDhs3bxJvfepM24PU3csTdm2mJ+4k2Z7fZr7EjR/8o8UbayQy8SfemPsKOfnp7t71eEoarWz2wuk1NVrftSxgGY/6CXknX2X3AMSLSKSItwMm4QXkAVPUVICsih/vQ2cASVR0ElgGn+/g5wBL/8z3+NX77Mr//UFxE5gNZVS3tLiu2BDhZRFpEpBM4Bri/gjolIqjSrDPwXWdxjiDfM+FzGWPMZBo10ajqGuAK4EHgSeB2VX1MRO4RkUP8bguAa0VkJdAG3ODjF+JmqT2LaxVd6eMLgfeLyAq/z+d9/DtAo4/fgEtaI5XtMdyMt8eBh4GFvryTI99HTABBw4RPtWV1ABunMcZMbRXdR6OqtwO3l8ROKPr5KeCwMse9AhxVJr4R+FiZeBY4d4RyvAy8rST2beDbI9egNoKoH8JmCEqHoMYu8jdtBrk3txnoMsaYqcRWBqiiIOqryowzgNgvQxPazDNjzBRniaaa8tmJ30PjxelpxAT2uABjzJRniaaKgig74QU1t5wsRZxqt3tpjDFTniWaKgqi7IQfEVAszkyzZWiMMVOeJZpqymcn/NCzYlF6mnWdGWOmPEs0VRRE1U00cXqaTQYwxkx5FU1vNiNrCrsJ85tJ5TcRp9tpid3ya+lgkIEJnDfOzCCIeiHX7R7QYIwxU5C1aKogzG8mWnMv9L9OPPAW0Zp7idbcSxD3T+i8UeNsd/6eF6tRTGOMmRSWaKooiAchrF4jMd/olnELu5+v2jmNMabWLNFUUzQAQfX6uOLMTPcAtJ4/V+2cxhhTa5ZoqinOEVcx0RCERI2zCbr/Ur1zGmNMjVmiqSLXdVbdUfuocQ5hj3WdGWOmLks01RQNVrdFgxunCfvXEwxurOp5jTGmVizRVEucJyCq6hgNuBYNQLp7ZVXPa4wxtWKJplqiQQDiqned7QJAqvu5qp7XGGNqxRJNlQRxzv9Q3UQTp2cQp1pJ9zxb1fMaY0ytWKKpltivAVDF+2gACAKitn2sRWOMmbIs0VRLoeusyi0agKjtnaS7n4XYnrVpjJl6LNFUSVJdZwBR696EgxsJBjZU/dzGGJM0SzTVEiczGQAgatsHwLVqjDFmirFEUyWB7zpLpkXzTgDS3c9U/dzGGJM0SzTVkmCLhsZZ5Bt2Ib356eqf2xhjEmaJpkqSbNEA5DoOJr35qUTObYwxSapoLq6InAlciXv81nWqemPJ9nnALUAHsBS4QFVzIrIHsBjYGVBggap2i8h04DZgL2ADcJqqviYiDcCtwCFAH3Cmqq4UkQD4FnASEAHnqeoj/tovApuKivNRVX11HO/FxMTJzToDyLUfTMPrv4Z8L6RaErmGMcYkYdQWjYjsClwNzAfmAeeLyP4luy0GLlLVfYAAOM/HbwJuUtV9geXAQh//GrBMVfcDbgau9/GLgR4fvwRY5OMnA/sB+wOfABaJSFpEZgIDqjqv6L/aJxn8gppQ/ftovFzHPAIi0t0rEjm/McYkpZKus2OBB1R1o6r2AHcBpxQ2isieQLOqPupDi4BTRSQDHOn3H4r7n0/EtWgA7gCO9/sPxVV1KdDpW0UnAneqaqSqzwOrgA8AhwKBiDwiIk+ISOH8tTfUddZQ9VOnUxGZNrcUTcumpbTE62iJ19EUdlf9WsYYU22VJJq5wLqi1+uA3SrYPgvYpKq5MscNHeO3bwI6RzjXcPEm4Je4ZPgp4BoR2a+COlWfv48mickAQb6XfNefiFMtBK/9auhR0WF+c9WvZYwx1VZJP08IFN+SHuDGSUbbXhqn6LigJD7cMSPGVfUnwE987GURuRs4Dqh4vZaZM9sq3XUrnZ3tW170bISMK157RxsEKRfPpGlob9r24PHEO5qhdU8aBtZs2ae1kdbW9m33n6Ct6lZnrG5Tk9Vtaqsk0awGjih6vQuwtmT7nDLb1wPTRCSlqnm/T+G4NX6/1SKSBtqBrqJzvVByrrLXEJGTgNdUdbmPB8BgBXUa0tXVTRSNbWmXzs52NmzY0ppoiftJZ3tpIKS7e3CoCI0dOfo3Z7c5frzxhsxcGjb9hu5N3RCkCTv66e2tbqumtG71xOo2NVndti9hGIz5C3olXWf3AceISKeItOAG5u8tbFTVV4CsiBzuQ2cDS1R1EFgGnO7j5wBL/M/3+Nf47cv8/kNxEZkPZFV1lY8vEJGUiOwN7AM8DrwNuEpEQhGZDXwM+MWY3oEqCeJc1Z+uWSpq2o2APGH/a4lexxhjqmnURKOqa4ArgAeBJ4HbVfUxEblHRA7xuy0ArhWRlUAbcIOPX4ibpfYsrlV0pY8vBN4vIiv8Pp/38e8AjT5+Ay5pgZtQsAL4E/BT4O9VtQ/4Lm685hngIeAyn/hqL4Gna5bKN7khrjC7OtHrGGNMNVU0F1dVbwduL4mdUPTzU8BhZY57BTiqTHwjrvVRGs8C55aJx8AX/X/F8RxwfiV1SFoQDyZ2s2ZBnJlFHDaSyq4mN/ruxhizXbCVAaolHkxm+ZliQUi+aTdSfauSvY4xxlSRJZpqiZJv0QDkW/YmzK4iyNk9NMaYqcESTZXUYjIAQK7tQAJiUrZCgDFmirBEUy3RIHGQzPIzW12maTei9HR7ZIAxZsqwRFMlQTxQk64zgoBc+wGku1dCftt7bowxZntjiaZa4lzykwG8XNsBBPEAqTceHX1nY4yZZJZoqiSo0WQAgHzLO9005w331+R6xhgzEZZoqiVO/obNIWGaXOv+pDY8CHE0+v7GGDOJLNFUSRAP1mTWWUGu/QDCgQ00rV1cs2saY8x4JD9NakdRw64zgFzHu8lnX6Ttuf9K1LAzA50fqdm1jTFmLKxFUw1xXNPJAAAEKbIH3UCu7SA6/nQu6bcer921jTFmDCzRVEM8SEAMNbiPZivpNt56z11EDTvT/swFW57yaYwx2xFLNFUQ9LnVlKP0tJpeN52KaM7kGJR/Jt37Zzpe/bY94tkYs92xMZoqCHvcc9qixl1qet0g30u05mGiOCbdKjT85VoG4jbCPU+GYHxPDjXGmGqzFk0VhN1/BmqfaIYEAf2zPwlRlqb1PyHIrrVpz8aY7Ya1aKog7HmBKLMThI2TVoaocQ6DM46g4Y2lZB4+muawmcHp72eg88PkG+eS7n6OcPB1et6xkDgzfdLKaYzZ8ViiqYKg5y9EDZPUminSP/uT5NoPJtM2Czb/hfTG39KglwMQEwAQDHSx+cD/A0EwmUU1xuxALNFMVJwn7H2RwenzJ7sk7sForXuTnjuf/jUPQ9shBANdBFEfUcPONObW0fTCNQzM+hD9cxdMdmmNMTsISzQTlOp9iSAaID9Z4zOjiBtmEhd+fsdnyXc9TPvKS8k0zyCadhAEKaJUO9nIJg8YY5JhkwEmKNWzEpjEiQBjEET99M06AWJoXv53tDz4bhp+fyph/9rJLpoxpo5ZopmgdCHRNMye5JJUJs7MoGevy+mbexaD0w4lvXkFTU98lmDwrckumjGmTlmimaBU93NETXMh1TTZRalYnJlGbtqh9O9yKtndPkO4+TmmPfEJwuy6yS6aMaYOWaKZoFSPErXuPdnFGLdc+4H0H3Q96c1Ps9PDB8LvP0d601Nu/baCfNbuyzHGjJtNBpiIOE+653lyM86c7JJMSLDL0fS13kNm1ffJvLSYGS/cStQwi2jaPOh5hVTvX4gzMxjY6Why0w8jDluIwzRBlIO4n6hhZwZnHEHcMHOyq2KM2Q5VlGhE5EzgSiADXKeqN5ZsnwfcAnQAS4ELVDUnInsAi4GdAQUWqGq3iEwHbgP2AjYAp6nqayLSANwKHAL0AWeq6koRCYBvAScBEXCeqj7ir30pcB6udXa5qt49/rdjbMK+lwmiLFHrO2p1yUQE+V7yb6wg3/4+Mge/h77X/ki6ZyWp3peIWt/O4OzjCPpW07DxIZr++uOy54gJiNqEfONcovQMICLMbSIOMuQ65pHreC8DOx0BqZbaVs4YM+lGTTQisitwNfBeoB/4rYg8qKrPFu22GPicqj4qIrfiPvj/DbgJuElV7xSRhcBC4DLga8AyVT1RRM4GrgdOBy4GelR1PxE5ElgEvB84GdgP2B/YG/iFiOwHvBs4C5iHS3K/E5HfqOrGCb0rFcq89QcAorZ3QnedjG9k2slNP4zc9MNo3NXfjwPQtA9MP4og30PD7PcwsO53EKQgSBMMvE6653nSQQ+p/tdJ974AQYo43Qr5fhpe/yUBMXHYQn/nR4ia9yDVrYSDGxjY6SgGdv44caqZVO+LhNk1hIOvE+Q2k2s/gMGdjnITLfI9BPkeCBuJU80Qx+5hc/EgRDmCeACiQYI4RxykiFOtEKQJBzcSDHYRDrwOb/XQ2BszsNPfEjfuPLnvszE7kEpaNMcCDxQ+vEXkLuAU4Kv+9Z5As6o+6vdfBHxFRG4BjgQ+URR/CJdoTvTbAO4AbhSRjI9/GUBVl4pIp28VnQjcqaoR8LyIrAI+ABwN3K2qWSArIr/BtXp+WEG9UgBhOI475F9/nGkr/pWGt35PNG0eccf+BANlVkwOGwga2qdWPN1I0JAZYf9p0LI7tL02FIlb5jI4/SCC2Ycy8Netn4uTmXsk+YE+ws3PkXp9KY1dy6DnT8TNexA370zL+h/Tuv4/tilSHGQIXv8pvHS1+zmuziMQOgBeuppcy75ATJB7kyDOE4dNxKG/TpSDIIQgJA7SQMol1XiAIOp341VBmjjMEKfaidPtEA0Q5t4CAvJNuxM1zSGI8wRRHzEZ4sw04iAk1fsyqb6XII6J0y3EqRbisAVSza5LMtVMEPUTDHYR5DZtKU+cJ4jd/yFHEOUI8r3u/GEztMxhWtgxtF8cNhIHTZBqIk61QJAhiPog3wuExOk2t2RSlAPygKtPQOwSetRPnGpz+8UxQb4PiIgzHUSpDgACXxbivO9GHfTvZQNxqpU4bHRxBgmiQf9FYNA/ziIiTrcTF1Y8j/r8sY3EQQNh7k3CgfXuvV43m9ZcE0E0QBBliYMG9yUmyBDkNxPketzvTJhxDx8M0sRhGsgUdfFmtz7/4Bvu/EFAlJnpvuj0ryPMriFKtRM170Gc2Ykg3+ves1Sj+3cKUv7fIA9xRBBHQz8Tx0O/NxASBwHgX8cR4eCbBLm3iFPNxJkZ7nxdGVr7+nEdNfixUD8+GjQQhw1F5Y/872kDQdRHkOt1v6OpFiAkGNxIONhFnGojapgJQUjYv4Eg9yZxejpRYydBrpuw92VomE73Pt8Y199Q0WdmqtJjKkk0c4Hir+vrgMNG2b4bMAvYpKq5kvhWx/gutk1A5wjnGin+eJl4JeYAzJjRWuHuxQ6l4agfAa6/rgVg9rvK7tkw64ApF28bJj6R8zP7ANj71KGXo6X3YJifqyXJwclaD3wW3p+GGl+3luq5w3Wy6laFlRnnAC9UsmMlfxMhQykWcL/XUQXbS+MUHVf62THcMeONV+Jx4AhccspXeIwxxuzoUrgkU/FjfStJNKtxH8gFuwBrS7bPKbN9PTBNRFKqmvf7FI5b4/dbLSJpoB3oKjrXCyXnGu4a5eJaQZ3AjTc9XOG+xhhjtqioJVNQyX009wHH+PGSFtzA/L2Fjar6Cm585HAfOhtYoqqDwDLcID/AOcAS//M9/jV++zK//1BcROYDWVVd5eMLRCQlInsD++Cy6RLgZBFpEZFO4Bjg/rG8AcYYY5I1aqJR1TXAFcCDwJPA7ar6mIjcIyKH+N0WANeKyEpcF/8NPn4hcL6IPItrFV3p4wuB94vICr/P5338O0Cjj9+AS1oAdwErgD8BPwX+XlX7VPUx3Iy3x3Gtk4W+vMYYY7YTQRyXDqMYY4wx1WNL0BhjjEmUJRpjjDGJskRjjDEmUZZojDHGJMpWbx6D0RYX3R6ISAfwW+AkVX1ZRI4FrgGagR+p6pV+v0QXQk2gXlcBp/mXv1DVf6qjun0Vt6xTDNyqqtfUS92K6vivwCxV/fRkLsKbQL0e9OUtrJH0X4B3UOZzIul/0yTqVy3WoqlQ0eKi83GLeJ4vIvtPbqm2JiLvw03z3se/bga+D3wctyjpoSJyvN99MXCRqu6DW1HhPB8vLIS6L7AcNxUdtiyEuh9wM24hVChaCBW4BLemXbXrdSxwHG4R1XnAe0XkjDqp2weBvwUOwn1gfkFEDq6HuhXV8Rjg3KJQ0nUoXoT3E8Aif2N4tesV4P7WDlbVeao6D3cT+TafEzX6W9xuWaKp3NDioqrag7u355RJLlOp83D3JBVWYDgM+LOqvuTXnFsMnDrMQqin+oVNj8TVbSjufz4R9y0K3EKoxxcthHobuIVQgcJCqNW0DrhUVQf8jb3P4f7Ap3zdVPUh4Ghfh51xvQzT66FuACKyE+6D9+v+dS3qMLQIr6o+DxQW4a169fz/fyUiT4nIRQz/OVGLv8XtliWayg23sOd2Q1U/p6rLikJjXaS04oVQgdEWQq0aVV1R+EMUkXfiutCiYa47permrzkoIl8BnsWtbFEX/27e93A3fL9RWp6S61azDrWq2wzcv9cncauSXADsMcYyVfP92G5ZoqncRBbwnCwTXaQUxr8QatWJyLuAXwNfAl4c5rpTsm6qehXuw2J3XGttytdNRD4HvKqqxctCTeYivFWlqr9T1XNU9S1VfR03XvTVMZapmu/HdssSTeWGW9hzezaWxUi3WgjVx8sthMowC6GWnquq/Fp69+OeovqDEa47peomIvv6wWBUtRe4GzhqmOtOqbrh1jE8TkSexH0Afwz4XA3qUKvfyfl+/KkgAF4eY5mq+W+63bJEU7kRFxfdTv0eEBHZ2//Cnolb8LQWC6FWjYjsDvwEN6voznqqG27m0M0i0uhnUX0c19005eumqh9S1QP8IPmXgZ+p6mdqUIfhFuGttunAt0SkSUTacRMezqL850Qtfl+3Wza9uUKqukZECouLNgC3+EU9t1uqmhWRTwM/Bppwv6CFwcUFuA+4DuAJtl4I9QciciVuEPUMH1+Im72zAnjTHw9uIdTv+Xg/WxZCraYv+vJfI1IYf+W7wJSvm6reIyKHAX/EPRfpx+oefb5hqtdtBEnX4S7gfbhFeMEvwlvtSqjqz/1Mzz/intFyo6o+MtznRA3+FrdbtqimMcaYRFnXmTHGmERZojHGGJMoSzTGGGMSZYnGGGNMoizRGGOMSZRNbzYmASLyNuAlYKmqfrBk2yLcPRedwN7AN4CZuC9+rwJfVNUVft8YeAY39TkGWnBLjvyDqi73U2av99eKcTcN9vhz/C7RShpTIUs0xiQni7tJb09/Yx4i0goUbs5LAT8HjlPVJ/z2s4AlIvJ2Vc37/Y72S5zg9/ki7h6Sv/GhZap6UtH2jwJ3i8juRWtlGTNprOvMmOTkgR+x9Q11nwJ+6n+OcHeXtxVtvw24CJeEtuGXHNkD2DjCde/HLVEyfVylNqbKrEVjTLJ+iFsS/uv+9bm4Z6dciuvq+ifgXhF5DXgEd0f5nao6UHSOB30X2ixcK+nnwGfKXcw/I+V84JniVpAxk8laNMYkSFX/AORF5L1+zbZ2VX2maPs1wGzcQ7zWAZcBfxSRaUWnOVpVD8I9MbIFeFBV1xdtP0JEnhSRPwIrcM8xOTnRihkzBtaiMSZ5/45bbHGD/7ngXSJymKp+C9dK+bmI/Atu8P9DbFkLCwBVfUJE/hG3ztUfVfVlv2mrMRpjtjfWojEmeYtxT0c8Hbi9KN4HXOlXHi6YA0wDni53IlW9A3gMuDaZohpTfdaiMSZhfuXv54C3VLV4EP9F3DPtvy4iu+HGX94CPqOqOsIpLwL+JCIfTqzQxlSRrd5sjDEmUdZ1ZowxJlGWaIwxxiTKEo0xxphEWaIxxhiTKEs0xhhjEmWJxhhjTKIs0RhjjEmUJRpjjDGJ+n+HGgKRtmcuuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['MSRP'].dropna(), color='orange').set_title('MSRP Distribution (clipped at 100,000$)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Average MSRP per country of origin')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msrp_dist = df.groupby('Country').mean()['MSRP']\n",
    "plt.scatter(msrp_dist.index, msrp_dist, c=np.arange(1, 8), s=200, marker='o', cmap='tab20b')\n",
    "plt.xlabel('Country of origin')\n",
    "plt.ylabel('Average MSRP')\n",
    "plt.title('Average MSRP per country of origin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Passenger Capacity`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     18366\n",
       "6      4172\n",
       "4      3304\n",
       "7      2235\n",
       "0      1969\n",
       "2      1937\n",
       "3      1780\n",
       "8      1168\n",
       "12      257\n",
       "9       140\n",
       "15        5\n",
       "1         1\n",
       "Name: Passenger Capacity, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Passenger Capacity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Passenger Capacity'] = df['Passenger Capacity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's weird. 0 passengers? Maybe it's the old data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1995.0    385\n",
       "1994.0    298\n",
       "1992.0    286\n",
       "1991.0    261\n",
       "1996.0    253\n",
       "1993.0    251\n",
       "1990.0    235\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Passenger Capacity'] == '0']['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will delete all cars dating before 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year'] = df['Year'].astype(int)\n",
    "df = df.loc[df['Year'] >= 2e3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31738"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0     17488\n",
       "6.0      4000\n",
       "4.0      3042\n",
       "7.0      2212\n",
       "2.0      1876\n",
       "3.0      1577\n",
       "8.0      1159\n",
       "22.0      241\n",
       "9.0       138\n",
       "25.0        5\n",
       "Name: Passenger Capacity, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Passenger Capacity'] = df['Passenger Capacity'].str.replace('1', '2').astype(float)\n",
    "df['Passenger Capacity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Passenger Doors`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    22970\n",
       "2     7992\n",
       "3      776\n",
       "Name: Passenger Doors, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Passenger Doors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Passenger Doors'] = df['Passenger Doors'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Body Style`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Body Style'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Body Style'] = df['Body Style'].str.replace('Crew Cab Pickup', 'Crew Cab Pickup - Standard Bed')\n",
    "df['Body Style'] = df['Body Style'].str.replace('Extended Cab Pickup', 'Extended Cab Pickup - Standard Bed')\n",
    "df['Body Style'] = df['Body Style'].str.replace('Regular Cab Chassis-Cab', 'Regular Cab Pickup - Standard Bed')\n",
    "df.loc[df['Body Style'] == 'Hatchback', 'Body Style'] = '3dr Car'\n",
    "df.loc[df['Body Style'] == 'Crew Cab Pickup - Standard Bed', 'Body Style'] = np.nan\n",
    "df.loc[df['Body Style'] == 'Extended Cab Pickup - Standard Bed'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4dr Car                                              9178\n",
       "Sport Utility                                        8086\n",
       "2dr Car                                              2513\n",
       "Crew Cab Pickup - Standard Bed - Short Bed           1672\n",
       "Convertible                                          1480\n",
       "Crew Cab Pickup - Standard Bed - Standard Bed        1371\n",
       "Extended Cab Pickup - Standard Bed - Short Bed        974\n",
       "Station Wagon                                         915\n",
       "Extended Cab Pickup - Standard Bed - Standard Bed     866\n",
       "Extended Cab Pickup - Standard Bed - Long Bed         836\n",
       "Regular Cab Pickup - Long Bed                         777\n",
       "Crew Cab Pickup - Standard Bed - Long Bed             746\n",
       "Mini-van, Passenger                                   491\n",
       "Regular Cab Pickup - Short Bed                        400\n",
       "Regular Cab Pickup - Standard Bed                     388\n",
       "Full-size Passenger Van                               371\n",
       "Full-size Cargo Van                                   369\n",
       "Specialty Vehicle                                     132\n",
       "Mini-van, Cargo                                       104\n",
       "3dr Car                                                64\n",
       "Name: Body Style, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Body Style'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Count')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Body Style'].value_counts().plot(kind='bar', title='Body Style Count')\n",
    "plt.xlabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Category'] = df['Body Style']\n",
    "\n",
    "van = ['Mini-van, Cargo', 'Full-size Passenger Van', 'Full-size Cargo Van', 'Mini-van, Passenger', 'Specialty Vehicle']\n",
    "\n",
    "for item in van:\n",
    "    df['Category'] = df['Category'].str.replace(item, 'Van')\n",
    "    \n",
    "pickups = ['Crew Cab Pickup - Short Bed', 'Crew Cab Pickup - Standard Bed', \n",
    "        'Extended Cab Pickup - Short Bed', 'Extended Cab Pickup - Standard Bed',\n",
    "        'Extended Cab Pickup - Long Bed', 'Regular Cab Pickup - Long Bed', \n",
    "        'Crew Cab Pickup - Long Bed', 'Regular Cab Pickup - Short Bed', \n",
    "        'Regular Cab Pickup - Standard Bed', 'Extended Cab Pickup', 'Crew Cab Pickup',\n",
    "        'Regular Cab Chassis-Cab', 'Pickup - Short Bed', 'Pickup - Standard Bed', 'Pickup - Long Bed']\n",
    "\n",
    "for item in pickups:\n",
    "    df['Category'] = df['Category'].str.replace(item, 'Pickup')\n",
    "    \n",
    "df['Category'] = df['Category'].str.replace('Sport Utility', 'SUV')\n",
    "\n",
    "car = ['2dr Car', '4dr Car', 'Convertible', 'Station Wagon', '3dr Car', 'Hatchback']\n",
    "\n",
    "for item in car:\n",
    "    df['Category'] = df['Category'].str.replace(item, 'Car')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car       14150\n",
       "SUV        8086\n",
       "Pickup     8030\n",
       "Van        1467\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `EPA Classification`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10950"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EPA Classification'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Compact Cars', 'Compact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Mid-Size Cars', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Small Sport Utility Vehicles 4WD', 'Small SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('4WD Sport Utility Vehicle', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Small Sport Utility Vehicles 2WD', 'Small SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Mid-Size', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Two-Seaters', 'Two-Seater')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Sub-Compact', 'Subcompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Small Station Wgn', 'Small Station Wagon')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Large Cars', 'Large')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Subcompact Cars', 'Subcompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('4WD Sport Utility', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Standard Sport Utility Vehicles 4WD', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUV 4WDs', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Sport Utility Vehicle - 4WD', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Sport Utility Vehicle - 2WD', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUV 4WDs', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Large Car', 'Large')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2WD Sport Utility', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Subcompact car', 'Subcompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2WD Sport Utility Vehicles', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Standard Sport Utility Vehicles 2WD', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Mini-Compact', 'Minicompact Cars')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsize Car', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('AWD Sport Utility', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUV 2WD Vehicle', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Two-Seater', 'Two Seater')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Sport Utility Vehicle', 'SUV')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsizes', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2WD Minivans', '2WD Minivan')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Special Purpose Vehicle', 'Special Purpose')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Small Station Wagons', 'Small Station Wagon')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUV 2WDs', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsize Sedan', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2WD Sport Utililty', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('FWD SUV', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsize cars', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUV - AWD', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Minicompact Car', 'Minicompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Minicompact Cars ', 'Minicompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Mid-size', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('FWD Sport Utility', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Sub-compact', 'Subcompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUVs', 'SUV')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2 Seater', 'Two Seater')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUV 4WD Vehicle', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2WD sport Utility Vehicle', 'SUV 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Sport Utility', 'SUV')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('MidSize Cars', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Small station wagon', 'Small Station Wagon')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Minicompacts Car', 'Minicompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Sub Compact', 'Subcompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Mini-compact', 'Minicompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('SUV 4WDs', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('large', 'Large')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Two seaters', 'Two Seater')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsize sedan', 'Midsize')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Subcompacts', 'Subcompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Compact Car', 'Compact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsize Station Wagons', 'Midsize Station Wagon')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Two seater', 'Two Seater')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('4WD sport Utility Vehicle', 'SUV 4WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Compact Sedan', 'Compact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Large car', 'Large')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Subcompact Car', 'Subcompact')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Two Seaters', 'Two Seater')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Minivan - 2WD', '2WD Minivan')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('4WD Minivans', '4WD Minivan')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2WD Special Purpose', 'Special Purpose 2WD')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsize S/W', 'Midsize Station Wagon')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Midsize Wagon', 'Midsize Station Wagon')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('2WD Van', '2WD Minivan')\n",
    "df['EPA Classification'] = df['EPA Classification'].str.replace('Minicompacts', 'Minicompact')\n",
    "df.loc[df['EPA Classification'] == 'Full Size', 'EPA Classification'] = 'Midsize'\n",
    "df.loc[df['EPA Classification'] == 'Wagon', 'EPA Classification'] = 'Small Station Wagon'\n",
    "df.loc[df['EPA Classification'] == 'Small SUV', 'EPA Classification'] = 'SUV'\n",
    "df.loc[df['EPA Classification'] == 'Pickup Trucks', 'EPA Classification'] = np.nan\n",
    "df.loc[df['EPA Classification'] == 'Light-Duty Truck', 'EPA Classification'] = np.nan\n",
    "df.loc[df['EPA Classification'] == '4WD Pickup Trucks', 'EPA Classification'] = np.nan\n",
    "df.loc[df['EPA Classification'] == '4WD Standard Pickup Truck', 'EPA Classification'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Compact                  4110\n",
       "Midsize                  3604\n",
       "SUV 4WD                  2150\n",
       "Subcompact               1766\n",
       "Large                    1628\n",
       "SUV 2WD                  1285\n",
       "Small SUV 4WD            1258\n",
       "Two Seater               1078\n",
       "Small SUV 2WD             881\n",
       "Small Station Wagon       836\n",
       "Minicompact               612\n",
       "Midsize Station Wagon     479\n",
       "2WD Minivan               407\n",
       "SUV                       398\n",
       "Special Purpose           101\n",
       "Special Purpose 2WD        85\n",
       "4WD Minivan                60\n",
       "4WD Special Purpose        10\n",
       "Name: EPA Classification, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EPA Classification'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['EPA Classification'].value_counts().plot(kind='bar')\n",
    "plt.title('EPA Class Count')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10990"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EPA Classification'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front shoulder room`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1335"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Front Shoulder Room (in)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Front Shoulder Room (in)'] =df['Front Shoulder Room (in)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34cff5d68>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Front Shoulder Room (in)'].dropna(), color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outlier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sport Utility                                        1359\n",
       "Crew Cab Pickup - Standard Bed - Short Bed           1225\n",
       "Crew Cab Pickup - Standard Bed - Standard Bed        1196\n",
       "Regular Cab Pickup - Long Bed                         697\n",
       "Extended Cab Pickup - Standard Bed - Long Bed         692\n",
       "Extended Cab Pickup - Standard Bed - Short Bed        637\n",
       "Crew Cab Pickup - Standard Bed - Long Bed             617\n",
       "Extended Cab Pickup - Standard Bed - Standard Bed     584\n",
       "Mini-van, Passenger                                   445\n",
       "Regular Cab Pickup - Standard Bed                     281\n",
       "Full-size Passenger Van                               278\n",
       "Full-size Cargo Van                                   267\n",
       "Regular Cab Pickup - Short Bed                        183\n",
       "Specialty Vehicle                                     116\n",
       "Mini-van, Cargo                                        42\n",
       "Name: Body Style, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Front Shoulder Room (in)'] > 62]['Body Style'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nah it's only pickups and trucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Front Shoulder Room (in)'] = df['Front Shoulder Room (in)'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front head room`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Front Head Room (in)'] = df['Front Head Room (in)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3317c0128>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Front Head Room (in)'].dropna(), color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting apparent outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mini-van, Passenger        3\n",
       "Full-size Passenger Van    2\n",
       "Name: Body Style, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Front Head Room (in)'] > 45].head()['Body Style'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Second leg room`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4551"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Second Leg Room (in)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Second Leg Room (in)'] = df['Second Leg Room (in)'].str.strip('-TBD-').replace(r'^\\s*$', \n",
    "                                                            np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Second Leg Room (in), dtype: float64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Second Leg Room (in)'].apply(lambda x: len(str(x)) >= 6)]['Second Leg Room (in)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34db30d30>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Second Leg Room (in)'].dropna(), color='purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front leg room`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1298"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Front Leg Room (in)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Front Leg Room (in)'] = df['Front Leg Room (in)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34d7459b0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Front Leg Room (in)'].dropna(), color='navy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011 BMW 5-Series 5-Door 550i Gran Turismo RWD           59.3\n",
       "2011 BMW 5-Series 5-Door 535i Gran Turismo RWD           59.3\n",
       "2011 BMW 5-Series 5-Door 550i xDrive Gran Turismo AWD    59.3\n",
       "2011 BMW 5-Series 5-Door 535i xDrive Gran Turismo AWD    59.3\n",
       "2019 Kia Sedona EX FWD                                   63.9\n",
       "Name: Front Leg Room (in), dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Front Leg Room (in)'] > 50].head()['Front Leg Room (in)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Front Leg Room (in)'] = df['Front Leg Room (in)'].clip(upper=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Second shoulder room`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Second Shoulder Room (in)'] = df['Second Shoulder Room (in)'].str.replace('-TBD-', '').replace(r'^\\s*$', \n",
    "                                                            np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a35015e898>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Second Shoulder Room (in)'].dropna(), color='yellowgreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011 Ford Ranger 2WD 2-Door SuperCab 126\" Sport    14.1\n",
       "2011 Ford Ranger 4WD 4-Door SuperCab 126\" XLT      14.1\n",
       "2011 Ford Ranger 4WD 4-Door SuperCab 126\" Sport    14.1\n",
       "2011 Ford Ranger 2WD 4-Door SuperCab 126\" XLT      14.1\n",
       "2011 Ford Ranger 2WD 4-Door SuperCab 126\" Sport    14.1\n",
       "Name: Second Shoulder Room (in), dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Second Shoulder Room (in)'] < 40].head()['Second Shoulder Room (in)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Second Shoulder Room (in)'].clip(lower=40, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Second head room`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Second Head Room (in)'] = df['Second Head Room (in)'].str.replace('-TBD-', '').replace(r'^\\s*$', \n",
    "                                                            np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3502081d0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Second Head Room (in)'].dropna(), color='magenta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Second Head Room (in)'].clip(upper=43, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Height`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Height, Overall (in)'] = df['Height, Overall (in)'].str.split().str.get(0).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3501be0b8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Height, Overall (in)'].dropna(), color='olivedrab')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full-size Cargo Van                  221\n",
       "Full-size Passenger Van               39\n",
       "Mini-van, Cargo                       18\n",
       "Specialty Vehicle                     12\n",
       "Regular Cab Pickup - Standard Bed      8\n",
       "Name: Body Style, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Height, Overall (in)'] > 90]['Body Style'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "432291.81481481483"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Height, Overall (in)'] < 45]['MSRP'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'MSRP vs. Height')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.gca()\n",
    "ax.scatter(df['MSRP'], df['Height, Overall (in)'] , c='orange', alpha=1)\n",
    "plt.xlabel('MSRP ($)')\n",
    "plt.ylabel('Height (in)')\n",
    "plt.title('MSRP vs. Height')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Wheelbase`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Wheelbase (in)'] = df['Wheelbase (in)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a350131940>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Wheelbase (in)'].dropna(), color='darkcyan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Smart    63\n",
       "Name: Make, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Wheelbase (in)'] < 80]['Make'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Width`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Width, Max w/o mirrors (in)'] = df['Width, Max w/o mirrors (in)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a350107080>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Width, Max w/o mirrors (in)'].dropna(), color='royalblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grand Cherokee    20\n",
       "F-150             13\n",
       "Taurus            12\n",
       "Ranger            12\n",
       "Edge               6\n",
       "Grand Caravan      2\n",
       "NSX                2\n",
       "Cullinan           1\n",
       "Name: Model, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Width, Max w/o mirrors (in)'] > 84]['Model'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Width, Max w/o mirrors (in)'].clip(upper=83, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Fuel tank capacity`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fuel Tank Capacity, Approx (gal)'] = df['Fuel Tank Capacity, Approx (gal)'].str.replace('GGE', '').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3500bbfd0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Fuel Tank Capacity, Approx (gal)'].dropna(), color='darkviolet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crew Cab Pickup - Standard Bed - Long Bed    29\n",
       "Sport Utility                                 2\n",
       "Name: Body Style, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Fuel Tank Capacity, Approx (gal)'] > 40]['Body Style'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n",
      "will be corrected to return the positional minimum in the future.\n",
      "Use 'series.values.argmin' to get the position of the minimum now.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2016 BMW i3 4-Door HB w/Range Extender'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(df['Fuel Tank Capacity, Approx (gal)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Fuel economy highway`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EPA Fuel Economy Est - Hwy (MPG)'] = df['EPA Fuel Economy Est - Hwy (MPG)'].str.split().str.get(0).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a35000f160>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['EPA Fuel Economy Est - Hwy (MPG)'].dropna(), color='dodgerblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = df.loc[:, ['MSRP', 'Country']]\n",
    "temp_df['MSRP'].clip(upper=2e5, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:448: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:448: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a3500236a0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LUGiSlcszH2Y0+frdjCaGpma5PJeF9OPnXJ6jV0ASG77rKDSJiMgCFJpk5Yp8xs4578NE8EaLJAVjPP1eAq3WYrvnWtM/3qaV4LvqaRIRkfkoNMnK5ZknbZjaXiOKDOMLfNrEGGg0CUepNNtzhyb6HtNMqH8ToKnQJCIi81NokpUbLs/VPCAe1ksa+pgaTU+/a6DVxs97jEqviE3gU5YAW0lYxpuyTCgiIlJSaJKVK3JIpowbMDE0+bQJQKPh6fXALLo81zRTK03lob30VG0SEZHZFJpk5YrMkzam5JkYmopGDE1N6PdMDE3zVZroFXMtzwGa1SQiInNRaJKVy7NYaaqb0ZTF5bkk7JhrNHzIUYssz/VjaJqirDSpr0lEROah0CQrV+Sxp6lOb2x5rulDI3hrsUZwZoQmWnGJsKuxAyIiMptCk6xcnkE6ZeTAoNI0aASPK3atFr7bnes9QiP4lLEGMKhEaVaTiIjMQ6FJVq7IPcm0T954I3jT0+9B0mpDvwd+juW0shF8mpZCk4iIzE+hSVYujByYvXuOxnD3XFEY8sYmwFxLdL4fRg5MM+hpUiO4iIjMQaFJVi5MBJ/ygEGlabg8B9BPtsI3nTmW6Pqzl+cGPU+7qjSJiMhsCk2ycvNWmnw63D0HkKcb4QG9OSpNvQLmrTQpNImIyBwUmmTlioypPU0mC+fOYcKD0jSGJhOX53anhybvfdw9N6PS1DBgwGv3nIiIzEGhSVYuz6dXmuh3odEaHG8SC05kSRuAYlZPUxaeZ6a9B2CMgVainiYREZmLQpOsXJExdU6T6XfxzXaoGAFpuTxnQmiiszP9DWJoIp398TbNRLvnRERkLgpNsnKzepoGlaaoEStNfR9Ck9+dHpp8FitH0wZollqJludERGQuCk2ycrPmNJksVJrKY1bKnqasCEFq5siB/nzLcwCmaUDLcyIiMgeFJlm5wdlzdcYqTWVPU79ogDGzG8FjpcnPEZpUaRIRkXkpNMlK+cLji8HGuEpmIjTFSlPfhKNU5m0ET+aoNLUSHdgrIiJzUWiSlcpjUWdqpSnrhZEDURp7k7K+wTRnH9o76GmaJzSpEVxEROak0CQrVcQq0NTdc1lvz8jwJIEk8fR7sdLU7Ux/kzlHDgBxeU6VJhERmU2hSVaqKCtN0z55WX9wWG8pbUC/D6Y1u9JEv9w9N2elqafQJCIisyk0yUrlgyrQlAdlvWH3d5Q2fFiem6OnyQ/mNM1xQa0Ecg+ZgpOIiEyn0CQrlWfha22lqcgxvhicO1dKU8j6QGtj5u65QaVpzkZwQH1NIiIyk0KTrFSRlz1NNYEm64Wvyd7luUbD0++Bac/TCF5WmubraQLwXT/7sSIicllTaJKVGizP1eWZrA+AT/eurQ2X59r4TofB5MvK14jv0ZjvGBUANKtJRERmUGiSlRo2glenJlNWmsYbwePynGlvgC+g1619j8Fwy7mW5+JjNBVcRERmUGiSlSpmNYLnsdKUTFaa+j2gvRHun9bXVB6j0qh/yECzXJ5TpUlERKZTaJKVKhvB62YolZUmbyYbwfMs9DQB0JlyaO8iwy3VCC4iInNSaJKVystG8LpPXuxpmhg5kHqyLC7PAcVufWjy/X00gu9qeU5ERKZTaJKVKmKlKa2rNA2W5/aGpmSs0uR3ZlSaEjC13eYj7zcITdnMx4qIyOVNoUlWqhw5YJKa3W+DRvCxnqYU8sxAaxMAv7td+x4+89BMpu2vG2pqeU5EROaj0CQrlc86F64cOTA2pylJw/OKZtkIPr3SZFIDfnZsMomBhgGdPyciIjMoNMlKDZfnqu+fNnIAIE/L5bkplaa+H1SQ5mFaCYUqTSIiMoNCk6xU2QheX2mKu+cmeppipaloQLMFc1Wa5ryoVqLlORERmUmhSVZqMHKg5pM3aASvqzRlYFptiimN4D7zmOYcO+fK92wmGm4pIiIzKTTJSpXDLesmgtc3gofnZZmBdhs/a05TusBHu5louKWIiMyk0CQrNTxGpeYBWT9MAx8rRZUDwvM8nj83bSJ45kNz95xMy+DVCC4iIjMoNMlK5TOOUTF5HxrNiZ1vw+U5gxmrNL3TXeDtn7gw+Nn3fX3PVJWWKk0iIjKbQpOsVJF5zLTBk1kP0uZED3fZCF4e2ltWmvLC855P7/DuT2+z04vVoqxYsNKUqNIkIiIzKTTJSuU5pI36GUom6+MbrdpKU79voNXGd0Jo+uxtfXYzT1bAh2/sAGWlacGeJu2eExGRGRSaZKXyzA/6kypl/Ylz5wCSOEF8uDzXAV/w8a90SQxcuZnw/hs68TUWrzTR93MNwxQRkcuXQpOsVBErTbXxJO9BozUxY6nMUXlmMK0NwEO3w8e/0uOeVzV5wJ3bfO50n1suZOEYlQV6mkw5CFNLdCIiMoVCk6xUUVaaalKTyfoTM5pgOHKgnwHx0N5Tp3e56XzGve/Y5h5Xhud87kx/ONxyXuWhvZrVJCIiUyg0yUrlOSTTls6yXvXyXLl7rh+GWwLceCYsx508knBiI8EAX7mQh6W2RZfnQDvoRERkKoUmWaki87XnzkGoNI2fOwfDRvAsM5h2OLT37HYYhLnZNDQSw7F2wlfO5/isWGh5rqw0aXlORESmUWiSlQqN4Ka+5zrvVS7PGQMm8eRxIjjAmd1QGdqKPUlXbCbccqEPmcfUTRyvUPY0aQediIhMo9AkK1U2gteq2T0HodqUZcPlubOdgqOthHLk05WbKbedz0O/1D4qTYVCk4iITKHQJCs1a+SAyaorTRD6mrL+cHnuXB+ObySDqtUVmwl5Py6xLdLTVB7uu6vQJCIi9RSaZKWKDJJpVaC8uqcJIE08eQY0WwCczdK9oWkjpVHEY1oWWZ7T7jkREZmDQpOsVJ5PqTQVBSbPqHtAkobddyZJoNXiXNHkaHv4Eb5iMyEtc89+GsG1PCciIlMoNMlKTa005X2A+uW5xJPHXFO0NrlAi6Ot4Uf4WDuhXZadFqg0kRpI1AguIiLTKTTJShWFJ6n71A1CU02lKYEiD2Fo+8iVeGPYGglNiTFc0Qr3m0V6mozR+XMiIjKTQpOsVJGDqVmeM1mYu0RS3wieZ+H785tXArDV3BuOrohhyU87367qvVuJeppERGQqhSZZqSLzJHVLZ1msNCXVIwdGl+fOb1wBwGZz70f4WHxtky740W4lmgguIiJTKTTJShU5tctzZaWpPjQxDE3t48BkpelofO3egtdlmqo0iYjIdApNslJF4esbwWOlqe6clSQNjeQYw7nmMYwv2BwLTUfia59f8LqMKk0iIjKDQpOsVJ6BqW0En1VpGi7PnWsc4WjvAhR7q0NHYoY6Xyywew6gmWjkgIiITKXQJCtVTJnTZAaVpvrluaJcnks3OdY7D53Onsdsxk/0maLucLua924ZCh3YKyIiUyg0yUoV+ZRK06zdc7GnyRjDdtLmaP8CdHb2PGYjfj29YGiilYCW50REZAqFJlkZ7z2+oHb3nBnMaaqpNKV+UGnapclG1iHt7u59TB7C0ukF849pJvhegfcLhi0REblsKDTJypSB52IrTRjYpcFmtosZqzSZLIamRcNPK4Fi+HwREZFxCk2yMmVomrV7rpgxEbwoDLs+ZSPrVISm0Jd0Klvs2gaH9mqJTkREaig0ycoUcemsrtI0bASvmwgenr+bgcewme+SjIUmYqXotgKKRapNcUimZjWJiEgdhSZZmUGlqXYieA9vEjDV95dDMS/0wjeVlabc4w30jWF7gaJRWWlCO+hERKSGQpOszKDSVJOZTN6PVabpoWm3H+7fyDoknb2N4GSeIi7/nVmgP2mwPKdZTSIiUkOhSVamPGzXpDVhJutBo1m7g61cntvuxdBkcuhW9DTF0HS6t0DVqKnQJCIi0yk0ycoUcXbStAN7fU0/U3he+Lrdj8tzSTHR02Ryj4kB6OwCzeCD5TmFJhERqaHQJCsza+SAyUOlqc5geS5WmtqJr2wEN6nBAGf7C1SayuW5XfU0iYhINYUmWZkiK3uapowcSJtQs3qXJOGOTtxk107AdPceo2LyAhqGo+mClaZ48K/vLDirQERELhsKTbIyMytNWQ8/rdIUxzd1+gkGT7NhMGMTwU3m8WnCkcTsr9KkkQMiIlJDoUlWZtDTVFNoYrB7rlq5PNfJYCMFmhuVoYkUjqZwbpHdc8ZA06gRXEREaik0ycqUlSZqJ4L3ZoSmuDzXM2w2oGi1J0ITWQGNhCOpWSg0QTx/TnOaRESkhkKTrEzZ05RMmQhed1gvDJfnerHSVDQ3SPq9kTQWh1umoadpt4B+sUBwaiXaPSciIrUUmmRlhhPBax4ws9IUvg5CU6sNgBkZcBmW5wxH4xrguQXHDqinSURE6ig0ycoMJoLXLM/NrDTF5ble37CRenxrI9w+OuAyj6EpVqXO5YudP6cDe0VEpI5Ck6xMPmP33MxG8BiE+pmhnQwrTUl3tNIUJoIficFskR10odKk0CQiItXq/1m/QtbaZwMvAprAq51zrxm73wKvBa4Evgx8t3Pu9MovVC6Kz6fsnvMek/UgmVZpCl+zDNqpp4iVJtPduzxXpIaj8bFn+gtcYCvBn13kCSIicjlZe6XJWnsN8FLgscBDgedba+8/cr8B/hz4FefcQ4APAz+7jmuVizOY05RW3BkPpptnea7IDW0Dvhl7mnZHludiT9NGAilwNluw0qTlORERqbH20AR8E/Ae59xtzrlt4K3Ad47c/zBg2zn3zvjzfwZeg1xy8nzK2XN5qPDMs3vOeGgmw0pT0tkePMbkHp8YjIlTwfsLzGqKIwfqBpaLiMjl7TAsz10N3DTy803AI0d+vjfwZWvt64CvBT4B/PjqLk+WZdpEcJP1wjdz7J4zBbQSj0/j8tyeSlMxmAO18KymVgKZD8uItRM4RUQuX9bauwK/DtwHaAF/CrzYObfYYLzq1/7vzrlnXOzrHKTDEJoS9p42ZoDRNZUG8ATgXznnPmSt/SXgV4HnLvImd7jD0Yu6yJMnj13U828PLvZvsLWZA+c5cWKDZmtvcvLFOTKgtdGmcbRV+xrGeJLCcHwzZWvjCN4Y2r5H88QmFB5TQLPd4MiRNifOFpzqe06c2Jrr+i4c36YPXHVkk/ToZHjTZ0B/A9Df4HL//eHy/RtYa5vAXwA/55z7a2ttm7A69AKWswL02CW8xoE6DKHpi8DjRn6+C3DjyM9fBv7FOfeh+PObCf9PWsipUxcGx3gs6uTJY9xyy/l9Pff2Yhl/g3Nnw+G6F3a6mLFB3ubMebYIR6T0L/TqXyRtYgrwvT7beZ+jrQ2yc+c5f3YX+gV3AnpFzvZ2l03vOdsrOHt2p/71RvRjX9WpG89hrtwb3PQZ0N8A9De43H9/OHx/gxUHuCcDn3bO/TWAc65rrf0J4C7W2u8mbOjywJuccy+z1r4EuNU595vW2muBtzrnHmGt/TjwTkJ7zpeAZ8XnXmGtfSvwduD7CXngJcAznXPPglCNAn7JOfcPq/qlRx2GnqZ3AU+y1p601m4BzyT8MUvvA05aax8Sf34asJY/llycYsruucHy3JTdcxCW9kwB7bIpfOQoFVM2fSfl8hx0PXTmnNVkyuqXjlIREanyAOCfRm9wzn0W+Bzwi8C/Ah4OPNVa+7iJZw8dAT4QN3dtA093zv0scMY5V/Y0bznnvoZQJPk6a+0Ra+1x4KvXFZjgEIQm59yXgJ8H3gt8hJBQP2it/Utr7SOcc7vAdwC/G9PpE4GfXt8Vy34NTjup+tRlcav/lEbw8rmmgLaJoam5MRhuacr+pdjTdDRdcCp4DE2a1SQiUskDVXNZHgFcHzd09YA/Bh4/47XeHb/+M2Gc0Lh/AHDO5YTK09MIWeC/7+O6l+YwLM/hnHsT8Kax254y8v0H2NscLpegIvOhUmQMnr3Vn7LS5GdUmnwCSWwEpwDf2iDphGU/YmjyMZSNTgW/E7Mbu8tKU7Gbr/9fEyIih89HgR8YvcFa+zDglcD1IzcbQr7w8XsIcxhHdePX0ceMGu2r+CNCsaQN/Mw+rntp9L8NsjJFEccG+IrlsrLSNCs0GUg9JFQsz8VlOF/unksWmwpumvE/Djp/TkSkynuBB1hrnwypn7nMAAAgAElEQVQQW2peBvwh8ERr7VXW2hbwXYTWmlPA18TnPnW/b+qcez/w1cAdnXOfuIjrv2gKTbIyRQ5JzblzwzlN9SMHIFSRGh58DF6+tYEZLM8V8TXK5bnwnLmngmt5TkSklnMuIyyRvdBa+1HgH4H3Oed+EfgF4G+AjxFmL/4V8BbgIdbajwF3nOMt3metfVfNfdcTBl2v1aFYnpPLQ5550qpp4MzfCO6TUGkqi1VFs03S3bs8VzaCtww0zfxTwcvlOb87bxOUiMjlxTn3GeD/qrj9jwjLaKO33QJ8w8hNL4q3XzvymJeMfP/0qveMow0eBTx7/1e+HKo0ycoUOZjUULmXLS7PFTMqTXkMTSXf2sBkfcj6E43gxoQz6OaeCq7lORGRQyUuAX4ZeLdz7oZ1X48qTbIyRe7DVO+KDGPycvdcTSmqfA0DzZFMU7Ti+XPdDiauqvmRmQaLTAU3qYHUUGh5TkTkUHDO7VC9u24tVGmSlSkKSBo1PU1zLs/lcfdcuddicP5cd2e4e26kb+poykJHqZhWAjq0V0REKig0ycoUWag0VS/P9WY2gQNkcU6TianJD0LT7mB5brzSdD7zg8bxmVoJXstzIiJSQaFJVmawe65qeS7rQ2N6aCo85InBVC3P7e5M7J4DOJpA38/fpmSaRrvnRESkkkKTrEyR+zCnqUrWw6etqRWhrg9zmijAxFzkmyPLc+VxKSOf6nIq+NkFpoJ7HaMiIiIVFJpkZabNaTJ5HxrT+5k63oRp3wWUTU2DSlNnZ7g8N9bTBPP3NZlWokqTiIhUUmiSlRnsnquS9SBt1jQ8BV0MRQLkTDSCm92RRvCxniaYfyo4zQSvRnARkaWw1j7XWnvduq9jWRSaZGWmVpqyPjRaU59fVpp8PnJQUdrAJwlJdweTx2A0GpriJ3yhSpOW50REpILmNMnK5Lmn0ao/RsWns5bnwkRwX4AvY5MxFK0NTHeXcmymT8rmpzARvGkWGDug0CQisnTW2scDLwW2gCuA/8c597ZYhdoFvg44DvySc+6N1tprgNfFx14NXOece7G19rnAtwBXEc6j+yvn3AtW9Xuo0iQrU+RgZi3PTdEte5og9jUFvrVB0tmFzMd+pmEwM8awlcD5eStNzQR6RfWhwiIisl8/DjzPOfcw4HnAL4/cdy/g64EnAq+w1t4F+B7gzc65RwMPAn7SWlueX/cY4JnAg4GnWWsftKLfQaFJVif0NE1ZnpsRmjo+9jQRBmUOXrfZHjaCNyaPaSlnNc2jPH8OVZtERJbpOcADrbX/Efhp4OjIfa93zvWdc18E/g54rHPuFcAN1toXAr8GtIAj8fHvc86dj9PCP0OoOq2EQpOsTOhpqrlzruU5A0lcghvp1fbl8lxeQGNyDtSRZLHlOUBLdCIi+2StfZy19ur4owEy4H8CjwT+gbBMN/ov6NGhMAmQWWtfCfwE8HlCVerWked0Rh7vx17rQCk0ycpMrzTNtzyXxk+sz4evU7TamG5YnqMx+ZHeSg0XFq40aQediMg+/SDwjPj9gwnVoPsCLwbeAXw7MPpP6GdZa4219h7AowgB68nAy51zfwJY4Jqx56yFGsFlZYocTO1wy/7MY1Q6Hhrx+UVhBom/aG6EY1Ryv2dGU+lIAjsFZN7TMDP+QdKMlaZOsbp/uoiI3L68DHijtfbHgS8CzwLuCHwc6APvAbasteVy2xbwIaANPN85d8paW77GLvCFeP89V/trTFJokpUpck9aM3IgNILPXp5LY2jauzzXxnQ74RiVigOBy1lNFzK4YsbxdmWlSQMuRUT2xzn3KULFaNRPxf8rvQDAWgvwJ86568Ze483Am2ve4rqRxz3hoi52QVqek5Wp3T1X5Bhf4JPpoanrDc2RStPg6a0NTJ5Bv9gzo6m0Fd/zfD7HEp1Ck4iI1FClSVamyH11aMp6AHM1gh9PQs9fMdYIDpD0ssqepiPlUSp9DxvTr9E0Q+jyuwpNIiIHzTn33HVfwyJUaZKVybPq3XMmhiZmVJo63gx6mvzoyIF2PEqln0NlT1O4bZ4ddMPlOe2eExGRvRSaZCW89/iC6t1zWT88ZkqlyXvoegbLc3k2ujy3CYTQ5Ct3z4Wvc40dKHfPdbLpjxMRkcuOQpOsRFkZqlyey0NomlZp6gMFhkbq97weQNEOoYmsuqepYQwbZs6p4KkBozlNIiIyaamhyVqbWGtXNplTLh1lD1LVgb3l8ty0RvBOeZZcHG5ZjBSCytBksgJfsXsOQl/TXMtzxkBT58+JiMikpYUma+2jCfMYbrHWftRae+9lvbZc+vIYWCrHJMXlucE8gQrdGJpaZWgaHW45qDT5ykoTwFZi5p4KbpoGr+GWIiL7Zq318esJa+2fzfv4w26ZlaaXA88nnCfzeuBXlvjacokbVJoqQs2w0lQ/RKkT/+PUiM8f3T1Ho4lPG5iCyjlNEPqatuc9SqWZ6Ow5EZHluBL42nVfxLIsc+TAUefc2+P3r7bWPm+Jry2XuKKIlaZpPU1TGsHL5bnWoKdpbzgq2psku6a20rSRGHby+YKQaWl5TkRkSX4duNpa+2fOue+w1r4UeBLhkN0bgX/jnPsKhBYfwpEr3+yc+1ScGP5J4D7OuU7N66/UMitN4/8r01via8slruxBqjxGpdw9N6WnaXx5Lh/b3Fa0N8EbfM0K32YCuwUUfo5qU8PgewpNIiJL8BPAjTEw3Ru4H/AY59x9gRuA55QPdM4VwB+M3PZM4O2HJTDBckOTjuqSWkVeX2kazGmacvZcZyw0+bGWo6K1ifFJbaVpMwlHYe/OkYVMM1FPk4jIkjnnPg38NPA8a+0rga8ntPSMej3w7Pj99zNyZMphsMzlua+21v553c/Ouacv8b3kEjPoaapsBF9g95zZ+3qD12+Gcx99Wv3vgI34xtvZcEJ4rWaC3+nPeJCIiCzCWvtwwnlyvwq8FcgZK7g45z5nrf28tfZfA3d2zn1g9Vdab5mh6d+P/fynS3xtucRNrTTls4dbdj20jSdNPMb4PbvnAIrWVvim5kDgzfi+O0U4hmUa00ootDwnIrIMGcOs8Xjgeufc/2utvQPwbVRnhd8n9EL9xmoucX5LC03OuT+ou89a++RlvY9cmsrKkKmcCF4uzzUmO+Oijje0TZgsbpLJSpNvxtCUVPcslaFprh10TQMKTSIiy/AV4AZr7XsJvUr/zVr7T/G+DwH3rHjOfwN+F3jjai5xfksLTdbahxFS4W3ADzjnbrXW3h14NfCtwOay3ksuPWWlKak8sLePT9I4xKk61HS8oZ2E41RMunciOIBvhPPnfFF9/MlgeS6fY8BlM8H3CowJ7yciIotxzpn4tQ88ZuSuR017vLXWAN8IvMs5d+NBX+eiltkI/tuEdPgZ4EXW2m8HPgYcAR6yxPeRS1A+pdJk8j40mnV5CQi759oxUyXJ5O65QaUp71Y+f1Bpmqe/u5lA7vGZqk0iIiv2KkIB5oXrvpAqy+xpOuGce6W1NgU+BTwL+BHn3H9d4nvIJWpaTxNZD9LWtMxEx8OJmLdM4vFjPU0+DZUmU1SHppYJ/0K4MEcQMrHb3Pc8RvVREZGVcc79JPCT676OOsusNO0AOOdyYAN4qgKTlIY9TZP3mayPbzSnroV1vKFlhsFroqcpLs/VVZqMMWwkC1SaQFPBRURkj4Oa03Src+7DS3xtucQVWdnTVNMIPmXnHITludag0lQRmtJ2+CbfrX2NEJrm62kCoKdZTSIiMrTM5bnEWnslMTyNfg/gnLttie8ll5giFm0qG8HzPjRatT1NuYc+hnIBzyRQjB2j4pM20Id+fWjaTAw7c4SmstLku4UmtoqIyMAyQ9ODgFsZBqVTI/d5YNZIQbkdK3uaqmqbJuvj55kGbspqlZ+oNGEaQB+T1U/b30zgwhwjBwY9TVqeExGREcuc07TMpT65nSknAVSPHOhBs1373E7MOc3RStPY7jlTVp6yndrX2UjgK/0FKk0dLc+JiMjQMitN5XyF1DmXWWuPAU8GPhbPm5HL2GBOU8XEbpP18Rvjxw8NlYf17glN40Wgcndef7v2dcrlOe89xtQvvA16mhSaRET2xVp7LWH697Vjt3vgB4AnOOeeG2+7Grge+E3n3K+v9EIXtLTqkLX2/sBngW+x1m4CHwReCvyNJoJLOacpqVqkzXtzLc+Vj6isNJX5Zkpo2kjCQUe9WcUmLc+JiKyEtfYuwLuBVx/2wATL3T33cuDnnXNvB7473vYA4NHAS5b4PnIJKnfPVRZ4sj4s0NNkyp6mkdcyZaWpN73SBOHQ3mlMS8tzIiIHzVp7J+BdwK86535r3dczj2WGprs75/4ofv+NwNucc4Vz7gvAiSW+j1yCBhPBK5fnejMP6wVolb3kSVjuMyOpqaw0md752tfZe2jvFA3NaRIROWAnCRWmFlB7du1hs8zQNPrP8scAfzvy88YS30cuQcOepoo756w07elpGi8CFWWl6XztkMyNwVEq00OTSQ2kRstzIiL7N/FfoLHvufwv4G8BXgF8GviVFV7XRVlmaLrNWvsQa+1jgbsCfwNgrX0M8KUlvo9cgsrltIk6U1FgimxqpanjDQ08yVhoGl3qM3HJzfg+plc9q2m4PDfPDjqD72p5TkRkn04zucp0p3g7wFucc38A/CDwHGvtt67y4vZrmaHpPxDWJt9D6G3atta+EPgfwIuX+D5yCSoyT5oy2dSU98PXZNrynKGdMPj3SZJ4fAGjEcwU4I0PwaxbvUTXjp/23TkKSKaZqNIkIrJPzrnzwL9Ya585cvPzCTkBoBcf92Xg3wHXWWvvvNqrXNwyQ9OnCI3fVwNvsNZeBXyAMHbgn5b4PnIJyvPqcQNkPQB8Wj/7tOMNGwa8H600jYcvPxifmnYuVL5OGZrmnQrudYyKiMjFeA7wAmvtR621nwAeCPzo+IOcc38KvJOQHQ71QQzLnNN0K3sPwhj9xTUR/DJXZD70M431G5my0jS1pwlayfCp5aG/fqQQZHIghjJTE5oahA/h7lznzxk1gouIXATn3KeAJ1XcdV38v9HHfv8KLumiLTM0vYHQAP424PXOuX9e4mvLJa6orTSF0ORnLM9tjvYvxdBUFGbwvck9vmEwOZhO9fKcMWGZb3eeApKW50REZMzSlufiZM+HAh8Ffs1a+/fW2hdYa69Y1nvIpavIPUlj8kxeUy7PTQlNHQxtM3ymScL3fiT8hEpTgseQ7NaPHQihaZ5KU4LvKTSJiMjQUs+Lc87tOOf+0Dn3ZOC7CJ3z77XWvmWZ7yOXnvqeptmN4B1vaI08NRlUmkbfwEPD4FubmG718hxA2xh2Z81pAu2eExGRCQd5yO7J+H93BFRtuswNds+N55V8eiO492G4ZWvktkFPUz423DKFonVkjkrT7OtVpUlERMYt+8DeuxG65b+PMOzyjcCjnHM3LvN95NJT19NkBpWm6kbwbngULTMMMMNG8NHQ5CFNKMwWSbf+KJV2Aqfn3j2n0CQiIkNLC03W2vcCFngL8L3OuQ8v67Xl0pfH3XMTcSX2NNUtzw3OnRu5LUnDqxRjPU0+Bd88gjlfn9E3jGE3nx2GTCuJu+c8FSM5RUTkMrTMStPjgQ7wPODfWmvL2w3gnXPHl/hecokZVJrGUtOgEbxmIng3hqbGnkbw8HW00kQBtAxFa4ukeyGs61WcDtxOoDNPAakZn9v3w+9FRGRu1toG8DOEFahy9NAfAC9zzs1R8j98lhma7rnE15LbmSL31efOzZjTNKg0+cnQtLfS5PFpCE0mz0IFq9meeL12ApmHfuFpJvVhyMRDe323wDQPsvVPROR267eAOwNf75w7Y609DvwZcBZ4zVqvbJ+WFpqcc59f1mvJ7U+RQ6NVv3uuqF2eC19HI9WwEXzktpFGcICkc56iKjTFS9gtYGoWao2EpqNTHiciIhOstV9FqDBd45w7A+CcO2et/VHgAfHIlNcCdyOsFfycc+5d1tqXAI8G7g78BvBvgH8EHgtsECpX/x64P/Aq59yrrLXXAK8jbDq7GrjOOfdia+1zCQcDXwV8NfBXzrkXWGvfCPytc+5347VeD/yMc+4Ds34v/RNaVqLsaRpXLs8xY3muObKulyRlT9PY7rnE4Ftb4TG1R6mE58zaQWfikpxRM7iIyH48Evhn59zp0Rudc5+Mx6b8GvD7zrmHA08HXmutPRYftuGcu79z7rfjz8Y590jgTwlB6l8Dj2N4ru33AG92zj0aeBDwk9baO8b7HgM8E3gw8DRr7YOA3ydsWMNaew/g5DyBCZa8e06kTpF7kqrlsKyPN0koH/nJJe5yea7N9OU5co9PoIihqW5W00Z8bqeY0eDdLCtNudrARUT2Z/Bf3Nba7wReROhr6hBaeu5nrf3F+JAmcK/4/XiAeUf8+nng/c65HeDz5fBs59wrrLXfaK19IeF8uxZwJD7nffHwYKy1nyFUna4HrrbWXksIT2+Y9xdSpUlWIs+orjTl/annznW9weBJR/LU6DEqw9cBUjNcnquZ1TRYnpsxdsA0h8tzIiKysA8B9499TDjn3uqceyjwNMIMxxR4onPuofH2RwH/FJ+7O/ZavZHvs/E3sta+EvgJQqj6ZcJZuOX/QHRGHuoJVStPaEj/HsLy3xvn/aUUmmQlQiN4VaWpB43ph/W2DVCxe64Y+Y+OiZUm39wMP9ecP1cuz+3MmtU0UmkSEZHFOOduIISRPygrQnE33bcR5ji+B3hBvP3+wP8Btvb5dk8GXu6c+xPC6KNrCKFsmuuAHwFuWGSWpJbnZCXCyIGKO7I+vtHEVyzNQVie20jYs3Q30dPk/aCniSShaG6S1Iam8HXenibfUWgSEdmnFwA/RThOLQWOEZbGvhW4APyOtfZjhKrQc5xz50fGFS3iZcAbrbW7wBcIVa6pO/qdc1+w1t5ACE9zU2iSlairNJmsN3N5rm32tjtN9DSVK2hJ6FMqWltTGsHD151Z5881B81P0x8nIiKVnHMF8Ir4f1W+reI5Lxn7+Qkj31/HSMhxzpn49c3Am2veY/TxTwCw1hrgrsBdgLdN/SXGaHlOVqKup4m8H5bnajJMZ0poKkcOmPjVx1Dmm1u1jeANQs127p6mnipNIiK3M88EPkoYc9Bd5ImqNMlKFLnHVOyeM1kPP6XS1PGGOyR+T6gqQ1Mel+dMGYDKClRri8b2rZWvZ4yhncDOrCzUKJfnVGkSEbk9cc69FXjrfp6rSpOsRJFBUvVpy2btnoPxmZjGgDF+ONyyrDTFxxWtI+EolRrtZI5KU2JCcNKcJhERiRSa5MAVhcf7uuW56T1NHW/2HNZbMsmwp2lYaYrVodYWJusOj2gZ0zYmzmmaoZlo5ICIiAwoNMmBK8NN1XBLk/VrD+vte8gxE5UmAJOOhqbw1cfXHx6lUt8MPmv3HIQddBo5ICIiJYUmOXBFFqo61SMH6uc0DY5QMZNVoSQZGTmwZ/fccCr49NA0Z6VJy3MiIhIpNMmBG1SEaipNJNWhqTyst1Uxw8kkfjDc0sRQ5k1ZaYpHqdTMatqYc3nONBLQ8pyIiETaPScHLo9VHVMV0fP65blhpWnyPpNAEfOMKXNNrGT55uxK01yb4ppGIwdERPYhnut2vXPu2rHbvXPOWGt/FPghwmBLD/yqc+4NI49rEAZVvtU59+Mru/AZVGmSAzfoaRofbun91OGW5WG9jYohTlWN4KO752DaUSqQeejPqDYZNYKLiCydtfZRwPOAr3fOPQT4ZuCXrbUPGXnYU4APAs+y1u73eJWlU2iSAzfoaRr/tOVhfc0n1ZWmMjS1akKTL3uaBo3g8c60QdFo1R+lUh7aOysPNY16mkRElu8uhArTFoBz7mbgO4FbRh7zA8CfEYLTd6/6AusoNMmBy8uK0HgjeBYPrp6xPFc1ciBJfO3IAQDfPIKpXZ4Lj5t9/pwawUVEDsA7gM8BN1lr/8Za+xLgVHlwrrX2JOEQ3rcBbwF+eE3XOUGhSQ5cXaXJxDlKdT1N0xvBJ0cOFCOvP+38uY3BsXKzz59TaBIR2ZeJ//KMZ75551zPOfcM4P6EUPRw4GPW2kfHhz4HeLdz7jQhOD3YWvu1K7ruqdQILgduEG7GG7pjpWna8lwTT0UfOCYZrO5NHKMC8SiVzpnK1x0sz81z/lyvoPZgPBERqXMaODF2252A09ba/xv4knPu3cCngd+y1r4U+D7g/cBzgauttZ+LzysI1aYfOfjLnk6VJjlweaw0TS7PxUpTTWjq+nBOnK8ILUnqKTIfgli5zDbyafatI7WH9pbLczuzZjU1TchLfYUmEZFFOOfOA/9irX3myM3PB95F2Ov8MmvtHQGstS3gAcCHrbUPB+4G3N05d23cffdU4HuttcdW+TtUUWiSAzfYPTdWajIzepo63oSqUMXyXJLGSpMxw+W5kZcvWlskvd3hm49ox0/9PD1NgJboRET25znAC6y1H7XWfgJ4IPCjzrnXA38C/F28/aPAh4HXERrAX++c2y1fxDl3PfAp4HtXfP0TDsXynLX22cCLgCbwaufca2oe91TgN51z91zl9cnFKfKaieBZF6jvaer6EHAqMlPoacrC9ovB8txYaAIw3W385vE9zy17mnZm9jTFs+w0dkBEZGHOuU8BT6q57+XAyyvu+rGaxz98iZe2b2uvNFlrrwFeCjwWeCjwfGvt/Ssed2fgFVDZ4iKHWFGxuw3iNHCYMhHcsGGqQ1OS+j2VJm/2vr6Ps5rSirEDDWNImbOnCTCqNImICIcgNAHfBLzHOXebc24beCthXsO43wP+00qvTJaibNiemNPUj5WmKWfPtSrOnYNQtSry2HOUe0gNfiRdDY9SqZ8KvjNr2He5PKdKk4iIcDiW564Gbhr5+SbgkaMPsNb+BPCPhK76fbnDHY7u96kAnDy59v6ztdvv3+DmLYCzHDvW5uiJ4UeuaHpyYOPoFmxMTmPqnjNsNQwbjcmPaSuusW1utGilu9AwbGw0KPIQcEwRluQ2il1aR9oTz99MMzJjOHGiftBs94qCLnAsXps+A/obgP4Gl/vvD/obXM4OQ2hK2Lun2zAy38Fa+0DgmYR10a/a75ucOnWBYo5DWqucPHmMW26pni59ubiYv8GZ06Gfb2e3R2b6g9sb57dpA7sdKMqm8Mh76PgmSVHQ2c0mXjPPUyDlwvk+jd2cRgq7u318ueRWtNkC+ufO0NnuTjy/BZzv5Zw9u1N73XkvXNPZmy+wxZX6DOg/B5f93+By//3h8P0NFOBW6zAsz30RuOvIz3cBbhz5+bvi/R8C/pIwu+F/ru7y5GIVNQf2lrvnqpbnQsyZvjwHUGQmROzU7I3eaROfNKYe2jvv7jm0PCciIhyOStO7gJfEsenbhKrS88s7nXO/APwC7Dk1+XFruE7Zp3xwYO/YHf1uGF2ZpDBWBexMOUIFhjOfityE3XPjr20MRXMTutuVz28ncCqbUXlsxN1zHYUmERE5BJUm59yXgJ8H3gt8BHiTc+6D1tq/tNY+Yr1XJ8swOEZlLNiYrAeNZuV+yDI0NWumcSdpuD2EJiYawQF8a4ukbsClMTOPUTEtzWkSEdkPa+0TrLXXj/x8zFr7fmvtK9d4WRftMFSacM69CXjT2G1PqXjc54BrV3NVsixlpWniHJWsB41W5Skl3UFoqjZcnmMQmsZfp2huknSre5baCcwsIDXK3XOzttmJiEgda+1R4J2ElaKfXff1XIxDEZrk9m10uKUfCSqm38U3WhMVIhge1tuoqTSV/VFFThw5MFk09c0tzPkbJ26HEJoyD/3C00yqR3+Z1IQwpuU5EZF9sdYeIfQjv8c59x/jbd8G/DJhteszwA87574Sz5r7AGFm4+OAbwF+Mj7uHwjTxDvW2h8jnFN3BOgB3+Occ6v4fda+PCe3f0UOmJoDexvVXUvdQU/TjOW5LA63TCeDT9GqrzSVU8F3Z1abDL6nSpOIyD5sAW8HHgS8CsBaeyfgtcAznHMPBv4O+M2R57zDOWeBk8APAY9xzj0UuBl4obX2OPAM4AnOuQfG16+cIn4QFJrkwBWZJ03BjDcvlT1NFbmoM+fyXN6nuhEcKJpbmLwXHjSmHRPczB10rUTDLUVE9ufrgHcD/5UwoBrCHMYPxnYbgN9h71ErH4hfvxG4D/B+a+1HgG8H7uecOwc8G/hua+3LgKcBFzeIcQEKTXLg8hyS1ExkI5P18FMqTQZPo+oMFUZCU2YwBfiKT7JvbYb3qdhBNzi0d9bsrobRyAERkf35e+fcLwM/DTzQWvvDTOYOw95WofKg3hT4Y+fcQ2Ol6ZHAj1lr7wb8PXAF8A7gOlZ4vJpCkxy4IvMkVd1z/frluY6H9pT/GJhkuDxHRug9Gn/fZpj2nXamhKYZYwdMM9HuORGR/ekBOOd2CD1ILwfOAo+OI4QgjBh6b8Vzrwe+w1p7J2utAX6b0N/0dcCnnXOvAv438B1UrjUcDIUmOXBFDkkyefKuybr4tP6w3hCaZlWawBS+8pM8rDRNjh0ol+d2ZuWhZqLdcyIiF8k59wFCX9NvEILSn1lrPw48AfiRisd/lHDe7HuAjxOC0a8AfwUk1tp/Jhyv9kngniv4FQDtnpMVyOsqTTMawdsJlTvrYGS4ZRw5UEypNFUtz23GkLWdz6o0GVWaREQW5Jy7nhCIRm8bDKsG/qLiOdeO/fx7DHuhRj15Gde4H6o0yYErivqeJqZUmjbM3hEFe55rAOPj7rmaSlMzVJqSzuQ5UeXuue1ZU8G1PCciIpFCkxy4cvfcRGrq96Yuz7WmfDqNgSQJy3OUwy3H+EYbbxJMRU9TYsLy3+xKk0KTiIgECk1y4Iq4e278RlNklYf1AnQ9tGsO6y0lDch7HuOrd89hDL65WdnTBKHaNCs00TSg0CQiIig0yQrkmQ/TwEfzSdYLX5MplatdWGwAACAASURBVKYZr5s0PL5X/lC91a5obZHUHNq7mRh2VGkSEZE5KTTJgauqNJkYmnw62SGeecgxtGZUmtIUihiafFL92KK5WdkIDqHStDNrY1wzgdzjMwUnEZHLnUKTHLiy0rRHvxu+VvQ0DaaBzxhXljR8nAJCbaXJN7dmhKbZu+cAio7GDoiIXO4UmuTA+aKipykLR5sUFZWmbswxrRntRkkDil54UGVPE+X5c/tfnqMRXlhHqYiIiOY0yYHLM09rc3x5LlaaKnqahpWmGY3gqcdvxzBTE5p8cyu8V57BWEDbSKBTQO496cRpwvE64xa+opMzs8lKREQAsNZ+J/BzhJyRAG9wzr18Sa99LXD9+FynVVClSQ5ckfvJSlO/7GmanH5fhqZGzTTwUtoAn4XvfW0jeJwK3tuZuK+c1TT10N6GludERBZhrb0GeCXwzc65hwBfTzhg9+nrvbKLp0qTHLg8CzOVRg0qTelk+aYMTa0ZoSlpeOhPX57zcSp40rlAvnl8z32bMWht53C05j8Jw0pTBsf1HxcRkTncEWgCW8Ap59wFa+33A0+11n6Dc+5nrLXfDLwVuMo5l1lrP0GYIH53wnErW8CtwA875z5rrf1a4HXx9T9avpG19s7Aa4G7AQXwc865d1lrXwJcA9wHuAfwe865l17sL6ZKkxy4ykpT7GnyFeerdMvluRmvmzTAxNW5mtU1inIqeEWlqTxKZbeYEs7K0DRzm52IiMDg3Li3AZ+x1n7QWvtfCGfHvQ14UnzYE4Fd4GHW2nsSDvI9TTg25dnOuYcRqlW/Gx//BuBn4u2fGXm7XwN+3zn3cODpwGuttcfifQ8Gvhl4FPCz1torLvZ3U2iSAxcO7N1726DSVHH2XCdmmGbNuXOltOFJYjVqt1Hw+RO75GN9UOVRKlU76MrluQtTxgmYZhmasqnXIiIiQ865fwdcC/w2odLzfuD+wAlr7ZXA44DXAI8HvhX4H8B9gXsBf26t/QjwX4CvttbeEbjaOffX8eWvG3mrbwJ+MT7+HYR/b98r3vde51zPOXczcBtw4mJ/L603yIGb2tNUU2lq4EkItdY6SQPKE+3ecp8v8qVrMq7YbfLYz1/JvW/bwmAGlabq0DRcnqtVhqapjU8iIlKy1j4VOOqcewvweuD11tofAv4t8E7gOwgHa/0F8Evx+xcTqlGfcc49NL5OCtw53j/6PyKj/4pNgSc6526Lz7krcDPwDKAz8rjx19gXVZrkwOUZmLF+7+Fwy+rdc+0EJg+rG9MsBpWm+1w4weM/exIDvP1+N/P39zgTXr+5Ed6v4vy5cnluWmga9DTtqtIkIjKnHeBlcZcb1loDPBT4MKGi9B+A/wV8BPga4L7OuQ8DnwSustY+Lr7ODwJvcs6dAj4fwxjAs0fe6z3AC+L73B/4P4R+qAOh0CQHLi8P7B2V9fDGMDn1MlSaNgz4Gctzn736DOWz73fqCHe/ucVTPnaSe916hA9cc4b/7w7bkKQUjVZlT1PLhH927Eyb9l0Ot1SlSURkLs659wL/CXi7tdYRwlBOqCpdD9yVMDLAE4LT9fF5XeC7gFdaaz8GfD+hOgXwHOAXrLUfZrj8BvDjwKPj498CPMc5d/6gfjctz8mB8oWnyCqOUel3Qz9TRbG046FtwlDMOl8+sc0NR89xP0K/X+wdJ8HwqM+c4Mxmj3fe61a+d7vFFc1NTG+y0mSMYTOBC9MqTcZA01B0souv64qIXCbc/8/eewZJdp3pmc+5PjPLu+5qb1FwhCFIkAAaIIYCzQztkJyZlRSSQqtd/VHExm7EhmI3pFBotbsR2l1pR6MJzYwozWoojeg9Z4YOhmZoYAnbQKEb6G60qXbl01x7zv64meXSVBW6qrvZ9T0RiOrKe/Pek4Wsqrfe7zvvNz7+OeBzbQ6Xlpz36RXP+wVwb4vrvdLm8XPAR1s8/s9XfL5vDcteFXGahE0lqwsSy1m5ey7ORVMLM2mxPNcag+GZfRcJsBbKc3pJTpNtFA8eGwDgh4cuo50AFTU7TbDGUSqeJU6TIAiCIKJJ2Fyyeo5S8+65GON4LUtwVaMI2gzgBZguRcyWInaXe7AXRNPy87sjh7vOdHOmJ+T1Q9tahltC3gy+6igV15JwS0EQBEFEk7C5ZGkuSOx2TtMKjMlFU6HDNd8cnsXSiuGqv+A0tQq3PHSxRFfo8Ph7d2M6OE2VNThNRpwmQRCELY+IJmFTyeqbzpp2zyUR2G5TeS4GNAq/XVilMpwcnmPHbDe2UdjKYMhLdiuxjeKuM91cGAx45VB/y+sVLaik4jQJgiAIqyOiSdhUFspzrXbPtXCaKjpXS36bYb0TfRUiN2PnVA8Yg2MbtFJtI8H3TRYYnk54/MhhtG6ODShaikrWeaeeci2MiCZBEIQtj4gmYVPpJJpaleeq9W1wfpuMpjeHZ/ETm76ZPH/Jtgy6QzyBQnHPazWmBro46V9sOl608wDNWqcUTU+J0yQIgiCIaBI2l0Z5zrJWRA6kMcZuTryodRjWm1qaMwNlds/0Qj1bybY0GapjDubeSzY9c1WeHbjcdKxYX1e5Q3al8ix0JKJJEARhqyOiSdhUGo3gK0UTjZymFVTqoilooYIuddfQlmFoZjHs1VYGbVQelNkOJ+A9z7zJWz0hk15t2aHFVPAOqsu1MGEnK0oQBEHYCohoEjaVRnmu5RgVp3mESrXR09RCw1zoqaIM9Mz7C4/ZliE1quMbOXMD3vWrE9gZPNd7YdmxYj10c77T0F7PwiQa9CoN44IgCMINjYgmYVNplOfslQN707jl3LmaUfjKLEQJLOVib5X+agFrSSnNVoYMhc7aO02ZW6BUixk7l3G05zLhkgsU698Bc53Kc/WhvUZKdIIgCFsaEU3CprJQnrOXPYjSGcZuXZ4rWM0RAqmlmeyqMVQuLmv6tslFUxp3EE1e3jR+5/EKqaUZ755aONYoz813ih2oD+0lEqdJEARhKyOiSdhUWpbnkjD/2Gr3nFYUVT6zbimXu2poC/rnlsde5qIJklrz4N8GxnLQls32i2X6w4CXei4tPl/lw4HLHXqaVEM0yQ46QRCELY2IJmFTaZUIrpIIoG15rmDlyeBLudDb6GdaLrQa5bm40l40oRSZW8CJKhyc7eVCUFnWEF601+Y0SVaTIAjC1kZEk7CpZEn+cVl5roNoajd37mJP3s9kp8vLcLbJE8Q7iibyZnA7rrF/rg9l4OXuRbepYKmOu+dUfXqwrnZofBIEQRBueEQ0CZvKYk/T6k7Twty5lT3jluZyd9jUzwRgGYOxFVF5baKpkDnsKndztGcSXe+bKlpQ7uA0LZTnZP6cIAjClkZEk7CptGwEr/c0rRRNoQGDahqhMtkVoi1D33zzGF8rA1wIq53fyroumgAOzvZRdRJOlGaAPHagU08Tfr54GdorCIKwtRHRJGwqWQKo+n91Gk4TK3bPLYxQWeE0TZVykdVdbi7nWZlBuYqw3H7+HOROkxVXAdhV7sZPbV7tngRyp6mWQdZuFEujp6kqokkQBGErI6JJ2FSy1GA7oNTq5blKm7lz06WQQuzgJs0lOEuD8iGJLHTcvkSXeQXsOARjsFDsne/hjdI0scooWvkUlraayFWgxGkSBEHY6ohoEjaVNDH5zrmlLk7DaVoRObAwd26F4zNViuirBRi9IrXbGKwMnGJ+fjjXHGGwsA6vgMJg1Ut0B+b6SC3D8a7phVTwchtNpJRCBbaIJkEQhC2OiCZhU9Hp8rgBWOo0rSjP6WanKbU0c8WI3lrQdG2l86qfXQQwVKeaBwA3yLx8Xp0TVgAYrhXoil1e7Z5cSAXv1Axu+SKaBEEQtjoimoRNJXeaWF5wS0IMCmMvFzkVk78d/SWPzRYjjIKuSrOLZNWNJ+WAX9RUp9uLptTLm8idKBdNCsX+uV5OFWdRbi6G5jrNnwssEU2CIAhbHBFNwqaSpQbbVctUk0oicL2mxu2agYIyqCXlualS7kp1V3xWYtV3vGlLEZQSytN2UyjmwjrqTpMdlhce2z/Xi1Fwpicfq9Jp/pzl2xJuKQiCsMUR0SRsKjrNh/Uud5oicIOm2O+qtprmzk2XQtzUwo9aN4EDGAVBV0oSK7K4eYcdQOqXAHCiRdHUFwf0hwHHei9jA3NJe6dJynOCIAiCiCZhU0kTg+XQ5DQZx2sKqlwY1quXOk0h/bVC07lQz2ii7jR15TZRNNdaNGnHxygLu1Ze9vj+uV4uFCsUbNPRaVKBOE2CIAhbHRFNwqaSJQbHXZGflITgNpfbakbl2//r+khjmGk0gbcSTXVxZZTBL2agOjSDK0XqFZeV5yAXTRhQXsp8h54mKc8JgiAIIpqETSWNDba3YvdcmjtNK+KY8rlzS9LA5woxmW3orjYLLFh0moylsCzwAk043ymrqbjQCN6glLpsqxUJCyFznUap+BYm0jQtWhAEQdgyiGgSNpWFnKalJFGT06RN7jQFS06drieBd7VIAodFp6meVIBXSKl1EE2pV8BeIZoA9s/2kgQhs1kHpymwc70Utj9HEARBuLER0SRsKmncXJ5r9DQtpRFsuVw0RVhaUQjbiKaFnqb8o1/ICCsK1eZtnflF7LBZNO2d70W5KWGmSNtsv7MKMn9OEARhqyOiSdhUsmTFsF7InSanzdw5lpbnIrojD6XbCJlGeW7BacowRpFUW/c1pV4RO6429Uf52qbP5ItsV6JTQX5NUxHRJAiCsFUR0SRsKmmLRnCVhOAsL881RJO3RDTNFmJ6Qr/lzjlYUp6rv4u9Qi5ookpr0ZR5RSydYSVh07EdSb6eV61y0zEAq1h3miodttgJgiAINzQimoRNJY0N1tKeJqMhiTHOimG9uiGa8p6h1NKUg4RS2LoJHJaU5+qXd/36c2ut39bpilEqS9kV52NaXmC29b0KdaepnLRdjyAIgnBjI6JJ2DR0ZjAalk1LSWMUBrPCaaotlOdy5oMYFJRqrfuZAOyFyIH6545BKUNcbd0MvnL+3FK6rPwiJ3SNhOZm70ZPk2431VcQBEG44RHRJGwaaZyLGntJea4xrBd7uRiqGoXC4NVLcbOFGIBiB9Gk6volq19eqdxtits5TX59lErUXIIL6k9JE5uj/nTTcXGaBEEQBBFNwqaRJnXRZC8pz9VFk7aby3MFBY0cpLlihDJQCNsP4bXrhpBeku3keJqo2mb3XIv5cwvXUuBbBifyeCa40HRc2Qo8SxrBBUEQtjAimoRNo+E0WUt0T8NpMitEU60xQmWJ01SKvIX5cq1QmclLc0sG/zp+RlRVTcOAATI3wCiF00I0ARQs6KoVedWboaqaG75VYGGq0gguCIKwVRHRJGwaab2StSxyoEN5rmjlfeIAs8Uo3znXJm4AwM7ArBgG7HqaOFRYNIsmlCJrMUqlQaHuNGXK8KJ/ufnpgS275wRBELYwIpqETSOLm8tzqr7df6XTlA/rrUcIYJgPYrqj9jvnAKzMoB2WRRI4vgYUady6GTz1ii1TwSEXTVFi0Z96PB1cajquAhst5TlBEIQti4gmYdNYLM819zRhr0gE14sjVMpBgrY6N4FDHjlgHLXMjXLc3KrKovaxA+3Kc0UbKincUu3jhDvHjBUtPyGwxGkSBEHYwohoEjaNRiP40vLcQk/Tkpym1EDE4rDe2WJ+zmqiyc4M2lbLAr4dN/8ki9uIpkIPbrV1FlPRNmgU++f7MQqeW+E2qcCWniZBEIQtjIgmYdNYiBxo4TSZJU7TQkZT/bS1xA3AotO0VDXZdacpCVuX5+JCD3Zca5kKXqqXB604YDQu8Gwr0RTqfLqwIAiCsOUQ0SRsGlnSXJ5r9DQtLc+tnDs3V4goxg521qKZewl2ZvLy3DKnqSGaWj83KfQC4JZnmo7VJ6VQTuHmWh/nnCrn7eri2kv1USrzktUkCIKwFRHRJGwai07TkgeTCGPZGGvxrVdtjFAxDdEU0x36GN0hb4C602QvF0eWDcoypFFrpykp1kVTZarpWMmu3z9RjIW9KMMyt0kV6wGX89IMLgiCsBUR0SRsGo3IAXtlTpMbLDuv4TQ1eprmgzyjaTXszKCdZkfJcTVJ1NppiutOk1duFk2+AhvDXAIl7bI36uLZ4BKm7oCpxtDeOXGaBEEQtiIimoRNY2H33IpEcON6y/qQqkvmzkV2RuxqimsSTWBaGEqOq0nC1m9t43ikXgG33DwqRam8RDdf7/W+Oexj2o444ea77VQpV396VkSTIAjCVkREk7BptN095/jLAimrRmFhcLShHORN4J3GpzSwUtNUngOwXdPWaYK8r6lVeQ7yEt18kj/3cNiDYxTPBhfztdedJj0Tr7o2QRAE4cZDRJOwaaSRwfFYNtJEJSG4/nKnSedp4CjDfJC7OGsRTXYG2molmjRJBKrFKBVoiKbmRnCAomWYS0EBnrE5GPbwgn+ZFI1yLPAtKc8JgiBsUUQ0CZtGEhscz1omkEgjjOuz1GqqNubOacN8PW4giFYRTcZgadAtynO2Y0hj1VY0xYVenOoc6OaG7pINldQsLPmWWh8VK+UVaxLI3SYtokkQBGFLIqJJ2DRyp2n5bDgVR+T20yILoslAOYgpxE7HQb2Q75wD0C3ewbarMUah09Zv76TYi8LgVppDLhsBl9V63MG+qItA2/zSmsjXX3QwcxJwKQiCsBUR0SRsGklscH21zFUijTArRZNWFBZ2ziV0R50H9UK+cw6aIwcAHKc+wy7pnNXkVZqbwRsBl+U0f66NxcGwmxetSTI0qmSjJadJEARhSyKiSdg00tjguMvDJ1UcgrN8EG/VLM6dmw9iilHnJHBYdJpMG6cpv3/rt3fcIaupEXC5VBcdCnupqZTj7hyq6KDnkmVDggVBEIStgYgmYdNIIoPjr3B7VjhNsYGkPncusTJCL6MYriFuIK3PmGvVCN5wmtqIpswroi0Hd75ZNHXVAy5n4sXr7o268IzFC8FlrB4HEgNVCbgUBEHYaohoEjaNNMqdpgV0hsrSZcN6l86dKzd2zq3WBM4Sp6lFBc526qNU2ogmlCIu9ePNX2o65FkQWIapJU6Ti8WhtJeX/CnorqeCT0vsgCAIwlZDRJOwaeS755Y+kA/rXTZ3Ti/OnZtfR0bTQk9Ty/Jc3YWK2r+9o65BvNlm0QTQYxum4+Vq7Oakn7KVMNGfq6lsMlp1jYIgCMKNhYgmYdNII4O9bFhvLjSWlucaaeAehvlCI6Np7T1NWYt3sGUbwLTtaQKIuwZxw3msOGw61m0bpuM8q6nB4bQX2yheHM7zncxlcZoEQRC2GiKahE0jqUcOLD6QCxRjL4qihblzdacpSBzsVeIGoLPTpFTuNqVx+1TwqDQIgD/X7DZ1O4ZyBsmSdfjY7IlKPN87Ba5CT4poEgRB2GqIaBI2BaMNaWKw3RZOUwvR5Ju8p6kr8laNG4DOThPUAy47jFKJunLR5M1dbDrWU99BN7cisuBA1MOkE6F7bPSUlOcEQRC2GiKahE0hTQADjtPCabKWiCatcDDYJneaSmsY1AuLu+e01Vpg2a5u3wgOpEEXme3hzzSLpu767ruVfU0Hom4A5vo0WhrBBUEQthyrd9xeBcbGxv4W8E8BF/g34+Pj/27F8U8A/xt5m8kJ4O+Pj483JxMK1w1pnAuPVk4TznKnqWhBqjKqfkrh8trekgtOU5tRKbbduTyHUsRdA3hzF5oO9ditRVNP5jGU+JwbjOl7AYwxbUe1CIIgCDce19xpGhsb2wn8n8AR4C7gH46Njd265HgP8MfAR8bHx+8EXgT++TVYqrAOkihvCLKdZQ8CzeW5ggXzfu7cFNfqNNV7mrTq4DR1GNoLeYnOn7u0fDYe4FpQsAxTLcyk/VE3b4zUINIgM+gEQRC2FNdcNAGPAI+Pj49PjY+PV4CvAp9ZctwF/tH4+PjZ+ucvAnuu8hqFdbLoNC0+tuA02cvLc0tF01riBgDsFLSjMLRxmpyG09RZNNlxDTuqNB3rtg1TLZyqA1EPF0fy2XP6gvQ1CYIgbCWuh/LcDmBiyecTwL2NT8bHxyeBbwCMjY0VgP8F+MP13mRwsOuKFjk83H1Fz78RWM/XIJ4JgSm6un36evOxKZmdoYGgu2vBgqqVLYYdRa07r7f1GH/5jrs2eDpGuwrbtnJZvfK4D8YoHNtBtZn+q/tGAOgKp4j6B5Yd6/fhdA1KpcWRL6WSz2E8Hht6C4BgTtO3xd4X8n0gX4Ot/vpBvgZbmetBNFksH+mqgKbfcmNjY73k4umF8fHxz633JpOTZfQadmW1Yni4m0uX5t/Wc28U1vs1uHQ+d2GiJGFmNv/f6c6X8YBqBEbHGAOVzMXVmmknxEttTFUTr+H/k4o12lUkcYrOWpxfb3oKKwbbT1teI/PzGXTq0gRh33LzsltZVDOHydmYwDGUSj6VSv6aBntLhIFm7vgsyaXeNX09bgTk+0C+Blv99cP19zUQAXd1uR5E0xngwSWfbwfOLT1hbGxsFPg+8DjwP129pQlvlySql+dWhFsa11+omMVAVp87NxHEa44bALATg3ZV2/Od+tDeLLaw/ZankHlFMsfHm7vcdKy3voNuKlbscJbfY3/czaWhCs6FMueTlzmRvsKF7C0u6zOAwsWnZPWwxx5jr3Mru52bsNX18K0mCIIgXAnXw0/yR4F/PjY2NgxUgE8D/7BxcGxszAa+A3x5fHz8/7g2SxTWS9LoaVoWbhmB4y00Xi+MUFGGcpDQXy2u+fp2ajC+vbKHe/F4XehkSYe2PaWISwMtZ9D11Z9/OYIdK5a1OypwaXiWgeNzfLX6pzi4DNk7OejeASgyEmb1JM/Ej/JU/H26VT/3+I9wp/cQngrW/BoFQRCE64trLprGx8fPjo2N/RPgCcAD/uP4+PhTY2NjfwX8M2A38E7AGRsbazSIPzM+Pv7fXZsVC2shCXPR4axoBDdusFCLbQRbOmgqfsLO6dXHpzRwUtBd7XufGkN70w7z5wCiUj9dl082PV60wFWGyyuawWedizzV9w36R45Q+tUQf0P9LsOlvWDAsFzBZV7CRX2GY8mz/Cj8Ck9G3+Xh4DPc5t4vUQWCIAi/hlxz0QQwPj7+eeDzKx77rfo/n+H62OUnrINW5TmScLnTVBdNqZtgFBTXMHOugZ0aEqeDaFrD0F6AuDSAc+4oVlRF+4uWklK523Sp/nyD4fXiL3ip51Fc7TPQmz++7dJusmKb2APlMmrvZ9Tez5Q+z4vxT/hu7c84mjzJBwt/hz5reM2vVxAEQbj2iBgRNoUkNKCaIweM6y+0/S+IpiBvsA7WGDcA4KQGbbcXTYtDezs7OnEp3zXnz082Het1DJMRaKN5Mvg2L/R+n23RAe6/9LfoKeS7MecurC2racDazvv83+Hd/geZSN/kc/P/gmPJ82t6riAIgnB9IKJJ2BSSyOB6K3KSGj1NdRo9TWFQA6AQrVE0GYOdgO5w+uLQ3lWcpq5+ALwWg3v7HENNw0+7/oLXvF9yuPwebrv8AezUozfwSG1DbWLt41SUUhxw7uCDhb9LjzXAN6v/jp+EX0ebNUwoFgRBEK45IpqETSGJNI6vlqVtqyTCOItb2apG4SlDuRDjZBZup6btJVhZPZfC7nye4xiSDkN7AZKgB205LUVTr5PHFryVTXNX7RH2zbxr4ZirFLMDGnW+dZxBJ0pWLw8Hv8sh9y6ejL7Lt2t/QmJklp0gCML1jogmYVNIQoPrq+W725IQ3CVOk1EUVJ4G3hV5sMYYrYVhvR3KcwCWq1ctz+U76PqbRJPBcHnn9wHYNvkwe6q3NzV61wYMfRcsam/DKbKVwz3eI7zT+xscS57ny5X/l5our/s6giAIwtVDRJOwKSRRXTQteUwlUdMIlaIFc0FSF01rU01O3dxZTTTlo1RWf4vnsQPLs5reHH6UqcGnsK0EM7+rSTABmH7FwLTN2drbH6dy2L2bB/yPcSE7xecr/xdlPfO2ryUIgiBsLiKahE1hoaepoTWMgbS5PBdYUPFjitH6ds4B6FXevbajGzOCOxKV+nFrc6g0L5Gd632O04O/YMfMPfTaMB23rgPa/fnjM2eubAbdLucmHgo+w5ye4ouVfyXCSRAE4TpFRJOwKSSRyXuaGmQJyhjMkuCmilE4Toa21h83AJCtKppWH9oLEJfqzeDzU8wGZzi27bv0Vw6w8/wDdNkp07HV8grJYP4xPRNj1uiStWPE3sVDwaco62m+WPl/mNfTV3Q9QRAEYeMR0SRsCklklg/ebVg+dt7TpA3UjAI3r7UF6xBNTn2Xf7ZKI7jtaoxRq1pSSbEvv158hld2fhk/7WH/2Q9hDHTbGdXMIm5xs6wESWAYmLCYNtma19+OYXsXDwafpqxn6sJp6oqvKQiCIGwcIpqETSEJDa67dO5cCICp9zRVjcKgyNy8JLbmuAHWU57Lz1s1dqDYh1bw/G0/I7Ujbjr7cVSar7O73kA1G7VQaEoRDcL28w6ndLjm9Xdi2N7JQ8Gnqeg5vlj5V8yJcBIEQbhuENEkbAoLkQN1VN1p0s6iaAJIgghbK/w2fUOtWCjPreY01Uep6KRzec7YLs887HJ5aJaD5z+IW+tdONZt5w7SdJueq2xAsf2Cw4mkttblr8rQEuH0JelxEgRBuG4Q0SRsOFob0hhsd2l5ru7EWLn4qNSDLaMgoivy1tUT5NbLc+kq717HbThNndXVTPdZXrgvYf94gd6ZQ8uOFe0MC8Nk2Poa0SC4iaJ8MSS9sramZQzZO3go+DRlPctXKr9PTVc27uKCIAjC20JEk7DhpFFjWG+z09RoBK/UnaZaqUZpHXEDAE5syFzFaulIDacp61CeS+yQ1w5+n1LZ44HvmqZoAUtBl5Mx3UY0xfVm8KHzNhc2OKByyN7BA8EnmNIX+Hr13xKbK9ulJwiCIFwZIpqEDacxrLd1I/hy/ZA3mwAAIABJREFU0VRpiKZ14MQG7SuM7iy01tLTdHzfj4ndKnc/dRPFcojVIqOgy06ZanONpB+MgtHzDieyjSvRNdhu7+U+/6NMZCf4VvWPSM3aZt0JgiAIG4+IJmHDScJcrNitnCY7z2mqaIWjDNrN1hU3AOAmBu1Z6FVEk+XkQ3uzNqNULvUf5+LQOLsuvBsvHAEgKM82nddtZ8zGCm2ar2McRdIHe897nMiq63oda2WXc5h3ex/iZHqUv6r9qcyqEwRBuEaIaBI2nCTOxYy1tKLVojzn2xoUBOsItoSG02St6jR1GtobO1WO7X+CruoIw+duIyz2AOBXmpuuu50Ug6Kctl5nPAjbJhzOZyG1K8xrasd+93bu8h5mPHmWH4b/9YpzoQRBEIT1I6JJ2HAaTpPTKnKg3ghe1Qqn3nNUaNMv1A43yXua1sJiwOVyju/7Eakdse/Uw6AhqoumoNxCNNV30M0lrUVTNAjFWUVQU5zRm9d3NOa+i1vd9/Ji/BN+Hn1n0+4jCIIgtEZEk7DhJFEuhpbvnoswygI7z2OqGIVyUyytCNaR0QT1RnBvjaLJ1STR8rf5xYHXuTR4nN3n34NX7gJA2w6xX8Sfb07i7nJSwDDdpozYaAbffd7dtBJdg9vdBzjgvIOfR9/hxfinm3ovQRAEYTkimoQNp9EIvqynKQ7BKyzskqsYC+0nlGJ3XTvnMAYnAb1Wp8le7jTFboVj+35EV2UbQxO3LDs3LPbgV5p7mhwFJVszHbcWdw3RdPhCgTfTyqaWzpRS3OM9wqi9nx/U/pw3k5c27V6CIAjCckQ0CRvOYiP44mMqrmG8AGMMWX2ESupHdEX+ukSGk+ST5DJnPU5TLjYMhtf3PUFmJ+w/+RuszCyIij34LcpzAD1uxkybvKesCFkB9p7zmDcps6uGIVwZlrK5z/8Y/dYw367+CReyU5t6P0EQBCFHRJOw4SQtcpqIa+AGwGIaeFiIKHVsAjd4zjwD3cfZM/IzDu/4Hgf7fgJAYfAofSOv4hWmgfaia+nQ3otDrzE58CZ7Jt6LWy02nRuVenDj1rEDPU7GTGLl+QIrUYpoBIZO599OpzYhemAlrvI44v82nirw1cq/ZVZf3vR7CoIgbHXW10wiCGtgqdOk63NsG04TZjGjSfsxhVrQ9HzHrrGt72W2D7xI0c9nrxkDcVbCKg8BUBh6jT2HTwCQJgEzF8eYmridsDyy/Fr1ob01ahzf+xN6yqMMnh9rue5wSTN4tX/bsmM9riY1ilrmUHCas5LCEeh/2jAYObzhVLnT6VrT1+pKKFhdPBR8isdqX+CrlX/D3yr9rxSs0qbfVxAEYasioknYcJLI1IMtFQsuUBxCqQ9YHKGCl1CY6V54nuuU2bftrxnpPYplZczVdvLGxYeZrw1TrvWTaYeB84Z3AGfOfITa3EWK3Zfo6jvLwOjLDO18ger8CBdP3cvc5UOAwvHzUtmx/qfRVrawW64VYTGfOee3FE25+ptN3JaiKRrJX+3tE0V+tm+e1DM4am0lxCuhxxrkSPBJfhR+hW/X/pjPFP9HbCXf1oIgCJuB/HQVNpwkMri+WtbgreIaui8XIo3yHF5ajxvQ7Bj8FXtH/hpLZZyfu4OJqVsph32wLMjR4NYnlSS2Ra08SK08yOTEzdjuEQZGjjO442X23f4XVGZ3MPHGg9Tm9wAwl1XYe/Z+nGqh7brjYi7gWsUO9NZF0+XQY3uheYdcVDe4Dp8O+PG+OU6bmP3KX8uX64oZtnfxbv9DPBn9FY+Gn+eDwd9BXQXBJgiCsNUQ0SRsOEmocX21bFOcimvgLqaBA+Am9Ol5bjn4LboKF5mu7Of4+QepRqW6QdVsCbn1dqPUMbDE8MkSn0tnb+PS2VsYHB1n+76nOfTOL6EG7+TUy79D16W99Fd206n/SdsOcVBq2QzuWYYeJ2Wi5nJ7f4vn+op40LD9DRvnQcXxtMp+7+qIJoB9zq3M6ylejH/KkLWDe/xHrtq9BUEQtgoimoQNJ4lzp2lBnhgNcYhx8v6lilFYlqZbG+7e/zmMsTl67mNcmtm9avyAH4K2IVXtzrOYnLiF6QuHGNn7HEcP/QpjfYrt5/fBvnTVtYcddtANujFnagWMUagW96+NQs94xk7tczwt84jbd1Udn9vdB5jXUzwRfpl+axsH3HdctXsLgiBsBWT3nLDhJGG9p6mhK5IIhcE4dafJKJQXs92aoBYP8KuT/w2XpnetKa/JjSAtqFXnzmnt8kJWYsIzuKUyQTbJ7u1Posg6Pi8q9hC0yGoCGHITEqOYSVo7SLUdoBK47XyRskm5bFYXaRuJUop7/Q/Tb43wnepnuZSdvar3FwRBuNER0SRsOAs9TXVUXB+h4riApuZMob2EYlTk+VOfpBY176BrhxdBVlh9WG9YnOX0zb+g9/IebOUxN7WDbQOvMLbve7hO+9TusNiDE4fY9TUvZdDN64EXal7r547mOvHQ8VxUHb8K0QMrcZTHA/4nsZXD16t/SFXPX/U1CIIg3KiIaBI2nLjhNC08kIsH7bkURr/JvLExfoqe2ofO1le+8kLQgeo4rNdgOHHHEyhjseuVI9h+SmV6kDfO3k8hmOTWA9+iq3C+5XMbM+j8crPbVLA1JTtjotpaNOmCIh6BvldhyHI5llbW9do2iqLVzQP+J6joWb5d+xOyq+x4CYIg3KiIaBI2nCQyuN5Sp6kGNng3/wK3+3XKcS+4KcE6B/VC7jSlQWehdWHfi8wPTLB7/AhWNcAtJMQ1h5mZg7x68gNk2uGmvd9joOeNpueGpbpoqrTvazpbc0l16zVU9oB7OuNAtcAFHTJrOpcDN4tBe5R3+R/kdPo6Pw6/ek3WIAiCcKMhoknYcOKaxvEX31oqLsNHweqdoDL5YWLt5hlNtfWJJpWBm0DqtxdNtdI0p8eepO/iPnrO7AfAKeY5BWk5oBb1cfTEB6mEwxzY9WO2D73I0h11UaEbAwQtBvcC7AtCYq14s9w6vLK6J89ruuuNPHH8tWvkNkG+o27MvYdn48c4mvzimq1DEAThRkFEk7Ch6MyQJeAsVLA07rbH4AAkFx5gcjLPTVJuihevszTXiBtoM3nFKM2bdz6OlTnsOnpkYaadW8h7kdJy3juVaY/xt97H5Nxedo08w97RX9CINzC2QxKUCNo4TQNuwoCb8tJ0sWXfejycz6IbOmoxYnm8nM5v6gDf1bjDfYht9h6+V/0vMqNOEAThChHRJGwocX2ESt4IbvC8r2EPvAF/DeHMfio6f8sVLbOm3XJLWRBNbYIyJg48T6XvIntffQi1pFnbCRJQhnhmseHcGJs3z97HxOStDPe/xqE9j2GpXFzVSn0Ec1Mt76EUHChUmE1szrUKylSK6m4IxlP2mQJTOr7qu+iWYimb9/ofIVAFvlH9I2kMFwRBuAJENAkbSlxrDOu1cN0f4ro/Iju7D54GY9lUdV6S6zJq3Q6MV9/QlrVwmqrdk5w9/DQD5w/SPbFn2TFlgdcVU51cuUtPcebinZw8/256S2cY2/ddHLtKtWeQYG4KpVv3I+30IwpWxssz7Ut0Vgi3nStiAUevYYkOIFAl7vc/TlXP8Z3aZ9HXqM9KEATh1x0RTcKGkoR5mct238TzvkWa3kX62ihGWRjLpmxy0dRj1h/62HCa4hVOk1YZb97xOHbis+PVB9AtxJjfU6Uy5UHW/Ja/NH2IY2ceJPBnuOXAX5BsC7CMbus2WQoOFGqcrblMx80KrroLjJWX6EYtn6PpfMs1XU0G7O3c432At9LX+En09Wu6FkEQhF9XRDQJG0qjPOd5f4XO9hJWPoSKI4zngVJMqvwt15W+DdEU5i3bib18vMq5Q89S7b3M3qPvQ4Wta3dBXwhGkUz2tDw+W97Ja6fej6VSdrz7eRiF4syltmvZV6hhK8PRJQOHGxhfEW6H4qsZe+0CZZNyykRrf6GbxH73Ng67d/N09ANei5++1ssRfo3QJiM1zYOqBWGrIWNUhA0lquQ9M45vU6t+Gq0VxFF97pxhRtngpJQii05z4FrhRZD5aplrM993nnOHnmPw3BilCzsxba7p94RYTsb8W30MjLRu8q6Gg7x68hFu2vMj7E/H9D13nElubb0Wy7AnCDk+H/DeIYVtLb9vdQ8M/lJzaK7A88V5nopn2R+sPcRzs7jTfZiZ7CLfq/0ZQ/YOhuyd13pJwnWKMZrj6QscS37FG+kLRKbGUG2UfjPKPf4j7HIOXeslCsJVR5wmYcMwJqE8+SgAlv1hsjRvxlZxiKkPr53HBi+lEK7/rZengS+OUMmcmDfvehS/1s2OV+/r2COlLENpZJ7Zs0V0GzcKIEq6efXkB9AzDr3vPslg18ttzx31IjKjuBg2N4TXducfe09YHLaLvJVVuaiv/V/qtrK5z/8YtnL5ZvWPiczVTy0Xrn9quszXqn/IN6t/xBvpC+xzb+Uu/330O0OcyV7nC5X/m0drnyc2zcn5gnAjI6JJ2DDC2S8TVeYAUNZik7RKIozrgTFUtY1yU6x4/T0+XpiPUMmyvDx38tafEhXK7H3pb0C8+lu5tG0ejKJ2ZrDjeWkWMPmzvXBasW/wcUZ7n6SVKzbkxdjKcLrS7CDF/aA9KL1hOGAXcFE8ncyt7YVuMgWri/v8jzKjL/Hd2n+6ppEIwvXHRHqC/1z+33krfY0HC5/gt0v/iDvdh7nJeRe/0f8JPlz4+9zs3sOv4h/xufK/YF637v0ThBsREU3ChhBXf0Fc/RGGWwCwl/RHqzgCN3edoszBc7K31RjtRZAF+SDgydHjTO56ndE378GfGljT891iQtBbY+bNvlUrg5XiAOqbhumZvezoe5IdPY/SyHJqYCsYcWNOVbzm9ARLEW6D4ITGUxYH7AKvpnPMt9mRd7UZsXdzl/c+jiW/4qn4e9d6OcJ1woXsLb5U+deg4KOlf8BO66am71VXedzpPcz7g9+jouf4SuXfSJSFsGUQ0SRcMVlyhtrsn+P4h0nTPVi2QS3p8VFxhHHz8lyaOhQs1p3RhKmPUPEUUTDPydt/TGlmGyPH72A9vVGl7XPEVYfkcm/H82o9/aBh8pUDTMzcyUDxJQ4N/wWWWt7Qvc2PmE8tKi0SN8Pt4FzUuDXFYaeEAv46ad1PdS047LyTPc7N/DT8BifTo9d6OcI1Zk5P8fXKHxKoIh8u/j0Cutv2CAIM27s4EnySGX2Jr1X/rZTqhC2BiCbhijC6SnXqj1CqQND9WyQhuAEsFTIqjjCOy4xtILPpUut3mZwELA2pb3jzzscxyrD3pfej9erPXUphoILtZsyf7OxOVbv60Mqie/IiZ6fv4fTUEXoKp7h5+1fx7MUy2zYvH9FypkXQZbg9/9h92qKkbG62u3g5neN0du130gEopXiX90F6rSH+ovpZZvXktV6ScI2ITI2vVXLh80jpb6LM2kYcjdi7uc//GBeyt6TUK2wJRDQJbxtjNNWZ/4TOJin2fQZjXOJQUTeVGidBvafpnFOPG2gz7LYTjYym88NvMD94jj2vPYhdbpHIvQrKgmCwzPz5ANUis2lh2bbN3OA2+ibeAmOYrNzCsfMfxHXmuXn0S5S8CQCKtqbLzjhb8ZuuEQ3lH4un84+3OCW6lcMP4suk18kvF1d53O9/nNSkfLv6x7KtfIvyw9qfM6kneKT0e3imuK7n7nQOcod7hNeT5ziayoxD4cZGRJPwtonK3ycNn6fQ8xFQuXOThAovYPEvziRGAcZxueDkYqk7e3sZTQCn9jzPwPlD9JzZ97bXXeivoTOLZLo5Y2kpM9t24VfnKczlw3vnwp28du6jaO1w0/avMdT1EmAY9iLO1RzMisBO4yuSPghO1VPSleJup5spHfNkMvu217/RdFv9vMf/Tc5np3gs/MK1Xo5wlXktfppXk6e4N/gAPYy8rWvc5L6LEWs3j1a/wIxun28mCL/uiGgS3hZp9CrR/DfwCndjuTfTKMc1nKaGaFJJbhEZx2PSqs+dS9dZUwPcKJ/fFhc0O44+sO6WqKX4vTWUZahe6Cyapkd2YoD+icVBt2HSx9FzH2e+toO9g0+wd/AxRryQxCgm42a3KRwG78xi8/eo7XPALvLzZJqXk/LbfxEbzE7nELe67+XF+Ke8GP/0Wi9HuErM62l+GP452+19HHDu7NjD1AlLWdzrfxiD4a+qf4o26/8eF4RfB0Q0CetGZ1NUp/8DtrMNr/R+lvYvJeFCjiVQ3zkHGNfNgy0Bh/XvIEv9vMY1ev69qGht/RbtsGyD3x1Svdi5DJH6Bcp9Q/SdO7ns8Uz7HLvwAc7N3M1Q11Hu2/VlwDBRbY4eiIbBnjME84vfau90uhm1fL4XX+KNtHpFr2Ujuc29n1F7P4/WPs9EeuJaL0fYZIwxfK/2OVKTcH/ho1csdEpWL+/03s/Z7A1eTER4CzcmIpqEdWFMSnXq32NMTKHvU00lqdxpWt4EDqBtl/l6c6nL+vpm5vtOYOsKsZ/iTr+98sFK/N4atVkX02J23FKmt+2iODeNV15ZTlOcm76bY+c/QK9/mdHCBBO1mJU7+aLh/GPpzOLXyVaK+9xeBiyPb0YXeCqevS4aaC1l8R7/twhUiW9V/1i2kd/gvJY8zcn0Fe4r/Bau2Zi0+r3OrWyz9/CT8Ovy/hFuSEQ0CesinP0yWfImxb5PYVo0jCahwvEWP2+U5+ZLHlnq4NjZuqanJN485w88Qff0AFnBRWcbk3Pk9+VJ2Mlk5xLd1OhetGUx+sqzLY/P1nbzytlPsrt0gXO1brq8J7DV4i+LeAiMgsKplVk3Fg+5feyxi/w4meKr0QUu6fgKX9WV46sC9/sfp2rm+U7ts2hzfeRKCRtLbEJ+FH6FEXs3O62xDbuuUoq7vfcTm5CfymBo4QZERJOwZuLqL4mrTxB0vQ9l7246bkzuNDlLWntUlIuTSz0uxC6BbTCsrQxg0EwcfBSUpufyCFlRLaSBXyleV4Rla2oXSx3PS4IiE/tvof/0G3RNnm95TpoV6ckG0cbm5Zkd7O79Ij3+i4DGuIpkAAqnm5Wipyzudbp5j9vHuSziz2pn+VZ4kbey2jV1ngbsbdzjfYC30td4IvzyNVuHsHn8PPwOZTPDe4IPrfn7ca30WkPc5LyTF+OfSZlXuOEQ0SSsiah2itrsf8HxDuEE74EW/Q9ZCkYrXHdJeS7Me3YudTsQOxSVWZgdtxqXdz5Nrfs8w6cfJqjYJIX177prh1J5ia68Sl8TwPkDtxIHRfa8+MuWrxugy04Zdms8dfFBqvEIQ8W/Zmf31wicc0TD4J3WLQM9lVLsswM+6g9xl9vLqazGl8LzfLZ2hsejSU5mtWsST7DfvY2b3XfzXPw4v4qfuOr3FzaPy9k5no0f41b/Xkr0b8o9bvPuo6BKPBp+HiNN4cINhIgmYVW0rjBx8l+jrCJB98fa/hCMa7moWVaeq1UxlsWlAqjYpWitTQBUes4wteM5eiZvxb+0Gz+CJNhY8eD31ogrDrravOttKdp2OHvL3RRnpxg6+Xrb8/YFc5RTl2fOf5wLcw9gWxV2dH8Tb+crWFVDMNP+281VFmN2gY/6wzzkDdJveTyfzvOV8Dx/WD3FF2oT/Die4uVknnNZSHQVfhG9w32QHfZBHqt9kRNJ+8HFwq8Pxhgeq30BV3nc5tz3tnfLrYarfN7hHuF8dpLX0qc35R6CcC1oP+5dEMgDLGvTnyVNpigN/LcY2u9cS6JcNNnLnKYKJigyGWiIXXx/9b6d1KkyceAxvLCfntP34tYMCkXirfrUdeH35qXDeLKboNg5pXt6dC+DJ4+x69Vnmd65n8xrFlqjXoVuO+EXk9sYDvZTjnfSV3id/l3HgNvY9fqvOHPPTkJnuO19HKUYVS6jlot2urlsMiZNwkUd8Uwyu6yQUlI2fcqhRzn0WPWPS/7tqSv7m8hSFu/1P8IT4Rf5VvVP2Bltx2Poiq4pXFvGk2d4K3uNI4WPY9F5E8SVss+5jWPpc/wk/DqHnXfiqM29nyBcDcRpEjoSzX+DNDrKwLbfBTrPa2s4TfaK8lxWKHLJNRht4VudG4sNhokDj6OdiOGTH8CkEIT5dTdaNLnFBNvNqF3sWv1kpXjrlndixzE7XvtVy1MsBe/sukg1s3l2aicGh+narZx07sNYmuCM4fDsl9k39y16ojdRqzRZW0oxYjncYhd4n9vH7wajfCLYziP+MO/xBtjrlLCUzYSJeTqZ5YfxJF+LLvCfamf5g+opPls9zTfCC/wsnub1tMK8Ttf/NVIeR/zfxlMBf3ruX0pw4a8xsQl5IvwyI/Yudm1g83c7lFLc6T3MnJ7iufjRTb+fIFwNxGkS2hLXniYqfw+/dD9B6TDRTKXj+QtOk7MomqywyvltA6Rp/lbzVmk6ndr+PNW+0wy/9TBU8n6joFa/vrOxpQSl8l105QsF+jWr/glR6+nn8q4DDJ98jYmxO0n95jEu/W7E4cIsr5f7uL03oMcL0VZAPGgwF25nOqgwFB1jb/JdUlVg2r+JWf8wNXskX1AHDIYACJTNoLIBj4bxp1DEQNVkhGgqJmNGp0zqiOPJYhZUv3LZYwccsovssQs4q9wToGh181DwKR6PvshXK3/A3yz9Y0pWz6rPWy/GGDAxxlQxJgKTAVneC6ZswALloFSAsgqAg1rD+oWcX4R/QdnM8HDwmU0ry61km72HHfZBfhH9Fbe7RyhanXerCsL1jogmoSVZcobazJ/heAdwi0dgDTts4rDhNC0+psIqZ0f3QpI/6Kr2kQO10nku73qKrumD+JcOLOzqCeoOVuhoWL9Z0pHCQIXqpS6S6R7cwblVzz+/b4zhM28wdPJ1zo/d2fKcQ4UZ3gh7eGVumPuG8lDOcCij+4THRecmLrqH6NEXGYhPMRS+yHD4AolVYs7dz7y3h4ozirbWl5tjMLhAr7LorYsLLB8oYYA5o5k2KRd1xNG0wgvpPD4Wtzgl7nS6GbE793X1WIN8qP93+cvJz/OV6u/ze8X/mYLVeech5OVdo+fQ2TQmm0XraUw2g85mMHoGoysYXcuFkq7BuoJPHZTdg2X1ouxeLHsIyxnBsoex3R0oq19EVZ3JbIJn4ke5xbuXLvqvmmgCuNN7iO/V/oyfR9/mkcLfvmr3FYTNQEST0ITRFapTf4RSBYLeT2D02pqOk7poWrl77uxIF07okgJuG/GV2REThx7FSUr0nT6yrNk8CBXagtja+ObnoL+KsjTVs730rkE0hV29zA5uZ+TEq1w4/A6M1WxPeZZmrz/Psfke7upzKTgJ4VBK76sewZRDOJQyZ29nrrAdO7ibnuw8vckE/dFrDEYvY4DQHqTqbKfmDBPaQ4TOAOZt9oQo6mJKeeyzPN5ldzNJypks4qW0zPPpPHvtAg+4fey024u1UX8PR/xP8NPoG3y1+vv8TuEf4eoQo2fQ2XRdGM2g9cwSYTRLs0q2UFYPlt2DZRfB6cdSAagAZfko5YFy8+nKpv71NRqUwZDlbpSOwURoXcHoMiY9Txy9AksGDitVxHJ3Ybv7cLzD2N5BLHvrOR3GGB4L8+bvd3j3X/U4ix5rkIPOnTwf/4S7vfczaI9e1fsLwkYioklYhjGa6vR/RGdTdA3+A4xe+8iSuD5U1/aWDOtNE84OFuiaKjAD+KrZSTAYzu//EYlXZvT1T2Li5e5AECqSImuOKlgPlm0oDFSZO1ui93bW1OV3Ye9N3PTcT+g7d5LpXQdannOwMMuJsIdj80Pc0T9BNJy/7u5zPuHQol2WKY9pZw/Tzh5UkFEy05TSSbqyS/TFxxiMXlk4N1ElIruXxO4hVQVSq/6fKpBaRTIVoJWLVnbesN+mEdxSMIzDsONwm1XgjAl5OS3z+XCCg5bNI7bFgElQJkTpGkpXUaaGqSYcjObxKPCoe4ovzf5jfjOEpR6VUgUsuw9l9+C6h1FWF8oqoVQJyyqCKoEqoJSq//LeuP+n+eWqGD2L0VPo7DJZcpG48hhx5Qf5a3dGsb1DeXSGN4blDG7Y/a9XXk+f41T6KkcKH0OZa/Mj/zbvPk6lR/lx9BU+VfwfrskaBGEjENEkLCOc+xpp9DLF3k9h6GU9v9TiqgXK4LgGrXOXKXEsLvS4jJzxsTHYJmsqwMyMvEx54E0Gz96HNd/cbB7UFOkGBluupDBYoXq5i3S6B2cNbtPs8A7CQonhk+NtRVPJThnxaozP93B73wRxv0Y7htJbLpfuaH1do2zKaoiyN8QFxsAYPEIK2QyBnsM3Vbxsnq7kNLauYa1SMjUoNDZG2eR+k0FhwGhyWapRaO4BfhObX7oH+bF7M/8xs3gweZ2Hk9dw0BjAqADjFAGfXdY2HjYD/Ng+zV+Wevht9wN0WUMoqwQ4dZew0/vGbIrboRSgiiirCIxiA24BjMkw+iI6OUeWniOtPU1SzWejWc4ojn8HbvAObO8gSt1YPxJjE/FE7csM2zvZbd9yzQbpBqrELe57eTH5CafSV9nr3HJN1iEIV8qN9RNCuCKiyhPElR/gl45guYfWHUoXVhR+AVD5L0QVVpnY1oexFCZ0KdlZU/pwtWuCi3t+Tml2H4Xzt7RMJw5CRTqsNq2s0CjRVc70ralEh1JM7jzAzuMv4VXLxMXWu+/2+nM8Nb+dC2Evo4VZ4kFNcGYdw4aVIqZA7BSYZXlJQwGWSnF0jGMiHB3hEGOZDKsuhhQay2Qok+WSqd7fY7AwddlklIXGQeOwXzkMqzmeUr38yLuFV7yb+bADI5YLWBRLLpVybicOAkf0fn6e/YyvpI/xKfcz9BrF+nqSrg5K2Sh7FMsexeEejDG5E5WeJotPEFceJa58H1QB178VJ3gHjn/HDVHK+2X0l8ybKd4X/INrJpga3OS+kzfS5/lR+BX+bumfoq4wEkMQrgUimgQAkvBFwtkv4Aa34xbuf1spvmFF4RdZEDfrPwpsAAAgAElEQVRWWOXMjgEA0tilaC9PA0/dKucO/QA36ab/1Pta39PkTtN0sHkNvQslunNFet/Bmkp0l3fsY+fxlxg8fZyJsbtanrPdq+KrjNfn+xktzFLbltL3io9Ts0kLubhQCQz9vIBKYerekLR7bcLQAJlxyJRDpIobGh5yFzBqFM8azZ+nhnstzXsszcofFyPWNo7wPn6e/TVfiP8rH/c+yQ5rx8YtZJNQSqHsQSx7EMe/C0/H6OwMWXKSNDpGEj4LKGzvEG5wF05wF7azMYOiryaXsrM8Hf2AW7x308Xgho9LWS+2cniH9yC/jP6So+kvuc29/5quRxDeDiL1BbL4FNXpz2K7u/C7Pvy2xx5E1eWiSYUVzuzspytW1FKbor14XaMyzh38AdqJGXnzQ019TA3cGGytSLzNbV4tDFZII5tsZm1b6eNiF3MDIwy+dazleBTIe4d2B/OcrBSYjgLKB2NUBkMv5VEKVqQ49O972fG9EtsfK3Lo/+vH6pyxedXYpgyPKDhoOTypNZ/PDBNp8/tiyBrkYec3cJTDV+Mv8Wr26jVY7ZWhLA/bPYBXfD+Fvv+eQt/fwysdAV0mnPsK5Yv/hPmL/4xw7uuElWO/FmNBjNF8v/af8VXAHd6R9QkmY8CkoEPQcf7vDXJ599g3M2iN8pPaN0jMdfJmF4R1IE7TFidLz1OZ+gMsq0ih9zNX9LMxqlgUe1hoZ1FhjbM7BhipebypbQpqcWfTpd2/pNYzwbaTjyzkMbWiUI8biDvviL9igr4qYKhd6qJrYA0lOuDyjv0cePlJuqYuUh7c1vKcmwoznA67+eXkDj68/U3ifk3/Lzwu3qHY9/luChMO0582RGHEtr/02fHjHs58cG3332w8pbgbzQ7L4VmT8afzIfdaivdYalm+U4/q4WH7N3hSP8n3kr9kQp/lIedhnF/D/qDchRrGsodxg/dg9BxZcoIsfoOo/H3OHP8uyurFDe7ECe7C8W9GXYdJ18/HP2Yie5P3F38HzPKSsNI1rPQyVjaDlU1jZbNYuoLSZSxdBZPkvW8r0CrAzHRRMkW03UvmDKLtQTJ3BLOG+AloBF6+j8fDL/JM/EPu8z+6Ia9XEK4Wv34/1YQNQ6eTVCZ/H4Bi/9/GmHX027QgrCj6ti3+sA2zGpMDg+w6VwIUgUrBwNzAMaa3v0jvpTvwJvegO/wVXKzkv5wjb3P/urccg98dUb1QpGuNYcnT23eTvfosg28dayuaPEtza2mSX5VHeHF2G6V7JtnxaIlb/+UAVgxzH1FMDc5jjKbrgEP/zz3OPaTQGzxn70rYpjSPAK+5Hk/GMW8Y+JAN25YIJ1/5HLGOcFQd5YXsec7pc/yW+xEGrF/v3WnK6sHx78Tx78Q3Ed7/3955R9lx3ff9c6e8eW3f9oZdlEUbNKKwd1FikWXJkWSJclQs0ZFlO7GOjx0rik8kn8SJ4+TEx3K3lGPZkR2qxZZkS6RISyIpEuwNBAiAGHQQuyjby+szc2/+mFlwASzAXWAbsPdzzu68uXNn3tw7981853fv/f3sE+RHPKql56kWnwLhYDkbsZOb49l48x9mZkwO8VT5uyyzXNpkAyI4gOn3YgbRnyHHzuRVGCizBmlkkFYToZFECSuefWmilESgEIQIVcUWFYSfx6ocJFF+/cxxQrOO0O4gsJcSOF0XFVHNZied5lpeKD/CNfbtZI26Wa0PjWYm0aJpkSLD0UgwyRKZhgdQanrOFCejUhAkJjzsj9VGD9XUWBT/JCEkleQAp7p+SirfTu74dW87ODVVjHqQy7YC/6JZLxuntsRoTx0qMBHW2w9olpbNUGsnDT1HeHPzzShz8p/TMifPYJBmx3AjY1mL99zVT64vSdmVDDXnz/jBGtpSInu4htbXspy8eWzSY80XCSG4MSFoCy1elpJvBJLrDcENhiAZiydDGGwSm2gSTbwUvMCD1b/nFus2rjOvx7gaBv0Kh1R2A9WgCzt9LyroJvSP4Fc8gvKrURazESuxFstxsRJrEWbTnDjYVMpHBicJqsd5OPg+UlW4ZewIWelF2xFIs47QbqNqrSeIhVIoUoCYcpdjJutQyFcAgaGqmHIUKxzGCvqxKodIlPegxiC02gmcVVST61Dm+aJoc+IOHi0d5KnKd/jZ1KdnsCY0mtlFi6ZFSCSYvoSUg2QaPoViaqb1ixFUIQwEdvKtm+/BFodkJcAoRs3MtMboXvsIhkzQeORdU/K7lC4KAhuqYvbHkTh1JeiuJxjIYbcOTWmf/o6VNJ04Sv3JYwx2rpo0jxCwNdNLxgzYm6/jX+rgltU90RT5CcWqNEkqjSG1OxKcvHkGCjQLtCC5Ryj2CIuXZMDrUnG9Idg8QTy1iTbute5jp9rF08FTHJD7ead1N+3G1ePUUAgTYS/HsJdjpd6BUkMov4cw6CGo7MIvPRflM+sxrWWY9lJMuxPDasUwmxDT9Pg+jpIlZDiADHoJgx5CvwcZ9CCDXkDyugXHHbgjrCOVWELJrCMwc4RmDVIZnOcK4pJ9ZanIJ5jZiG82QmIVKIWlxrD9UyT8EyQLT5MsPE1gteMnN1BNroe43DVGPevsG9lTjQaEaxcEmisFLZoWGTIcigRTOEim/hPAzJjGy3E3mh1bmhSKQx01dJ3MU/AjK8PprocI7RLt+9+PqkxtHEiqKPCzs+ejaSJOTTnyDt6XoXaKommsoYVKKkPjsQMXFE0QCae1qUGUgjfyddTYVa6pOz/47djKKk0vpUgNJig1VC+5LLPJ+FinVabFXgVPy4DnpWK9EKwzBB0CkiLFjdxIp7WU18JX+Vb166wzN3C7dQc14sqfyj8RIQRCNIDTgOlcg50G1BDS7yEMTyD9U1Qqu5goToSRi7yiGzUIswaEg8CKwt8QDcRW+ChZRsmx6C8cQanixG/GMBsx7XYSyY0MmiYvBo/RZXXRbt9EfqJpVsGliaNpVQSByBE4OUrOWgxVwvG7SVSOkso/RjL/JNXkBqrpbUirmfX2TRwP9vHj0oM8kP0vWAtwbJhGcy5aNC0iojFMf4SUY2QbPomaIcEEUIm70ex4llu/VWU063D73iH2SxPTLFOp7ab18M8g8lN/aKaLAr9OIOdANAkDkrVliqfTkXfwKe0k6F+ygiWH9pAojFHNXLxsa1OD5EObHUNNdKRGaXDOnkGUX+XT9FKKxl0puu9amKJpnBySmwVsMC2OIngj9Hk9VKSBpSISTx1GO/cY7+awOMgbwV4OhB6bza3cYN1IRly+hXMhEjnZrMd06jEZb0ghKhxCyRGkHEHJUZQsIGUBWe1FKR8IUCoARDS4XJgIkcQwMhh2E0ZiRRxjrwYhsgiznnFnor6q8kj1GzgiyXX2Dchglvuyp4AUKUqJNZQSa7DCEZzqQRLlPTjlXQR2B9XUNq5NvIsnK9/lhcoPuS35/vk+ZY3mbdGiaZEQ+t0UBv4MVIVMwy/G3r5njtJobGlyItF0OFkAoKO/yrMtBQyrSNOJW7EG25nyG6+KLE0jS+Yu6GqitsTI0TSyZGOkpvbg6Vu6mvYjb9B+YBfHtt520bxCwDWZPvr8pTzTv5SfXXIQc0LxghpFqS0g96oF7wCugHizOSSbgY2GQb9hcVJBjwzwpASpSGDQLlwaxEqqooeXwzfYFe5kg7mRbeY2Go35Hzw9+5jR+CazaRI/L9FFPnfsU2QcunD3WTTTVaKU4lH/EQbVAPclfw6ChedgNDBrCVLXUXQ2kfTfxKkcJD36EGuMDMdTzTxfeYTV9jZazWXzfaoazUXRomkR4Jf3UBz6CsJIkql/gMgWMLMURqJHgZOJbvBHEnlaT4/w5srTFMZMspYiedKdVnR1pwJWKKjO4UyyZF2JEaA6UEOyc3BK+/jJNH2dq2g5tp+Taza/rbUpYUi2ZPp4YayNPcOtbK4/fdb2sVU+Lc9YZHsd8q1Xji8bU0CrCmgFtghF1UwwhMEgkl4ZclxaSLUcWI5NwMlwkO3hy7QJmy3GCraYy8kYi/GWFHdpX6K/jxfC5zgoD3BL4k5yYXrenVheDGU4lJw1lBKrscNekpWD3F7o4VQKfpD/Iz6R/hxJe+l8n6ZGc0EW4x1qUVEtPEVp5OuYdjup2vtRsxSwszgSxZ1LpCRlJelOVnBPnWLv2m6C7c00pksoUZjWMbNj8cy5y5/YN2XsdBXTDin1ZqcsmgBOrtxAc/chlnivcfTaO942f7tTZHk1z47hBtpTozQnS2e25bt8mp9N0rAzRf6+K0c0TUQIgaMC2oA2YIMATIsxTEYFDKkE/TJBv2pmVAn2h/AP4XEcJM0iQYNIkBMWtcIkh0lOmNQKi3phkbwaZuLNEPtDj+eCZ3GtDSxVHcgFGMZmUoTAt1rxrVZMOcZt1Z38mBM8NvJfucvYipN5dxwL8AowtWoWFVo0XaUoWaE0+k384jPYyQ042feg1OzdgAojglQWhFAcSxSRBgysPE3jyY0oZZI2qtMeh5odi8635Mg5C2kmRGRtyvemqVPx+JQp4CfT9C5bS9vRfYy0djDUMXkg34lszPTTHyT5l1PLubO5h2WZyM2ATClKHSE1O024lyuii25qSGqQ1CjogDgegU0RwSkGOSkHGFWSPpVlQNUQkMSfpPA5TFoNm1Zh0yoSdBoJOkQCe5GJqUPhQR7xH2aJ2clW61rCcGGPgbsQoVFDbfJ23OBV9toHWVrZy/KB1zDtLpzsu7GS23ScOs2CQYumq5AwOEVx8CvI4ATJmnuxnG2zHvqhOBJ5A1dS8WztmxgErPEGyBc/AEBKVC5BNBmENpTMcE7jwCZqSxT6sshCCjNbevsdYrrXbCYzMkDXK9uRps1IawcgSOZHaDq2n1xvD2bgU6xt4Oi1d5KwE9xR28NLY+083ruUVZkRrms4RdoKyXdVadmepuaUw1j7lWltmhqSNLCSWlYatVQoMkQ3fXIXRTUCJGgQq6k1VpAy2ihjMqR8BqTPS2GBCpHQNIEO4bDMSLDMcFguHJqEjXGVWioOh4d4yP8+zUYrt9t3XrGCaSIbzC30hoM87uS537mVXNmjOPQVDLOZROZuEunbLtlVg0YzU2jRdBWhlKRafJLy6HcQwibT8EkQLXMSK6swbFDToHg29xS9divLho5x+xN1/L9rkxgoElQIpnnM7JigkhMEczywdTykSuHNenIbpi6alGlycNsdrH/+R6x5/scEdgKhJGYQoIRgtKmdcraWupNv4m7/IftvvY9kMs2tuR4Olxt4o5DjeKmGrXW9rFs2TLOhqNuZortGkHR8HPsK6Xq5DBzStLGWVrGGkjHCgDrOgDzKYLgXERo0iiV0iC6uNVaQNlooGdCnAvqUz2nl82JY4OkwElJpDNYYKdYZKVwzRf0VGNZlMvaEu3nM/zFNRgt3Jt6FDOd/ptxMYAqTW8xbeSJ8nIfYwy/k/jWpsA+/9Arl0W9RGfs+icxdJDLvxJjEYaZGMxdcHXcRDWFwitLw3xNWD2A560jW/Ew8fmluBlEXRwXlFQd5o2YEVCsf/IdjlHINjAUpamwfpab/wM/mDcpLxJScYM4klhOSaSkweLAOafhURx2CUoJkU4G6tX0Y9uQiVElBsZTjuaUfJTk6Rrs8QCpdoJypZbBlCUEiekvOLVnB6h1Ps/qFx9h3x3sxDYM1qQHaE6PsLjTz4mAbO0QLn2gYoOGFkB9ZyxACmusLbF3bTW12csuTUjBWdMgXHXKZMtn0lWt9EEKQpo60qKPT2ERJjDLMSQblcXbL7eyW28lQR7PRSbNYymbRgW3kkKKGMQG9BJxSVQ6HJXYGBQigVdhcY6S51szSbiTmu4jTRirJ9uBJXg1fodNczs327airRDCNkxZpbjFu5cnwp/wg+AEftu8nmVuJDE8RlF+lkn+ESv5H2KmbcLL3Ytod833KmkWGFk1XOEqWqOQfpZL/MULYpOs+jDBXzmkk9r78KNVSHcW6I5TlGlaUHJp7hzm6pIvRIEWbUyWc5swgpwzJsmC4Zn66V2qXDRCUEgzta8FKhqRqAkYONFI8maP5+uM4dWXKgylGDzZSHkwjqyZKnj3u4jgbqVszQF3H6bPGRo02tXN0042s2vks7Qd2cdLdCkDW9Lmp5gQjYYqeao6TSy1W7ajyntohjuRsjpxO85OX1rKqY4Dmujz5ksNIPkmhnEBKwVghiR9G8QOFUKxcMsiWNT2Y5sKJY3cpRAKqljS1LDHWUaHAKL2MqF6Oy30cZTcCQZ1ooV60US/aWSraWCMy3G6mGUHRjc9xVeHxcISfhCO0iwTXmhmuNTI0GAvfqeKQHOTH/o/oUd1stq9lnVh/1ViYzqXBaOBGbuL58Fm+53+H99sfxDHbSGR+Fjt1G0HlNaqll/BLz2A5G0mk78RKbkZcJZZEzcJGt7IrFKUCqsXtVMZ+gJJjJFLXkUjfgcKCOZxyfCDcz2Pde3F4gPqG1Rw0Rll3POrS6q9poiptsub0Zs0B1A9EAiSfnZ8HvumENF/TjakSmAmJVCGZ9iQD+1s4+eRKrEwVP+9g2iG17RVMu4JpSUwnIJGpIlXIyPFahg80YZhQu+5stwKD7cupO93Nkn07GGntpFgX+ypSkJNl6tIljJUQvu7gHixR/JlB6utNjp9s5VB3EweONwOQdkJqUiEJC5raKtRlqti2z4mBFId6GsgXk9y65TCWOTttwh42cPpNCit8Zmli5nk4ZGimi2bRhTIkRTHMKH2MytMclbs5zE4AUtRQL9qoFU20iEZWiyak2cxRI+BgWOLhYIiHGaLLSLLNyLDVzFAjLi9o9UwTKJ9Xwpd5IXgeC4u7nPtoUU1IdXUKpnE6jA5u4hZeCJ/nu/4/8kH750mKFMKoxU69A8u5ibD6OtXSDopDX0YYWezUTSTSt2FqlwWaWUSLpisMJctUi09Ryf8EJYewEqtxaj4Kom5OrUsFVeAp/0n2yb009d5DAHjtFep9i85jJwA4nWqFIqTE9GfO1Q+aSBPyTgjzNA5aCJCiigzANAV2TYnmzccpnWxC+hbZFSOkGkapnPPGPz52K7u8lyCEwX2NJJvzOI1ni8djG66nZqiPrpefZPedH2Bgdwtjh2pQoYHTUKFxWz9Dy0Ia3jBJ3CUgGbJq2Qm6Ok2Capq0EyDMKoEMz6veZUtGSCVzvHG0madfW8VtWw5hWzPbPpqeSbLk4QxCCaoNkkMPDFNtnlsfQUIYZGggQwPthotCUmaMPIPk1QCD6iQn5IEz+RMkyckmukQdq4xGhkQDJ6TPd4MB/ikYYI2R4hojzTojReM8WqAqqsLO8DV2BK9QpMhqy2WrvQ0CiZz26MBLo6AUgwpGUAQKHAGNfki9UthzMMC+0+jE5DaeC5/h29Vv8V7752iKHaEKI4mVvAHTuQ4VHCeo7qVa+CnVwmMY1lIS6duwUzdgmLlZP0/N4kKLpiuE0O+mWnyGavFZUEWsxGoSmfchjHZAwhwJplCF7A538UzwND4+19s3Ue7bQncyZKyuwj39jaSGBvATDidUIwaKtFGZtpPi+kGDUqOg4i+scTmmLcku6z2zXrlIuYSA2hV9lIeSDO5cQttdB5g4czpMOBy55iZWvfQ0A4/WMVrM0dhVwUqGDB1LcuLxJTjr+2jyfVa87rD/hjIAhhGSSI5Fj85JLrsIQ0DQ3DCKYSj2HGlh+47V3L71EIkZGkxetzNBx0NZSusVpRUBdT81Wf21erxfHyRMz193oMAgRS0pamkWXSAgJKDMGCVGKTFMXg7TLfcTELWtFNBOLT6rOSbb8WRkKa1BscQwWSFSdIgMTYaNCG1CpTBj0SCVooqigqSqomUFRVVFy4qSGAIcDDLCpFFY5DAnndVXURXelMfwwn0ckYcJCFhudrHBvoYamUUGsyuWykrRreBNpXhTKib1UpavYAKdQrDJgNVCnKmL2aDdaOcO7uSF8AW+WX2Qd1n3sMHceMZ/kxAGwl5Owl6OnXon0t+PX9lDefRblEe/jZlYg53chp3cimEtBs/zmtlGi6YFjAz68Ms78UvPE/rHAJNE6hrs1PUIoyG2LM2NWApUyOvBLl4MnmeUUTrNZVxv34wdCrafSFBoKbGpWINTCkgPDzFW18CJcj3tyTJymtOhnRLUDQn6NxlzPgh8pjFMRW1XPwP72igebSaz8uwgvSON7TxlP8BovpF1q/dRXl6LlIqWJsHQvkaO7G1hZeoUS14MObgF5AXGL1uVKq0HjtJy8Cj1J04TWhbHN6/D3rwesVKx53Ar219eybubX8TOQKm9/pLLlOgz6PxuDZXlir53VKiGFQr3mnQ+lGH5P9Vy+KPDC8q3lIlFhnoy1APLI/9QCkLDp6IKVESRispTUkMU5VFGCSnQSIUmDsp6PCQQWwnPXD6FOPN/elhAThjUIElSxiBPRfVS4iQGRbJYrLM3ssJcRVo6yDCcceuSUooxoF/BCaV4UylOq8ggbAMdps06wyKHIIXCjMVh1TbpqQa8KQMeDiU1wLWGYrMhZs361Gy0cLe4h1fky/woeBRPetxp3UmT0XxWPmEkMZ3NmM5mZDhI6B8kqOynPPptyqPfxrDasJxNWM4GrMQqhDHzkRE0Vz/iUl33X0GsAI4MDOQv+QHc3FxDX9/YjJ7UZChZIqgeJKzuxy+/jgx6ADDtDhKpbZiJ1ShlM1cz4gCG5BC7w13sVXsoyiKtRjub7Wupl7VIAo6NGez/g43kb+/nmvVFnIFeNj/0zzyz9T4ezd7JdbkBEnJoWt+58oDF+j023l2CQb88SyWbHqYpCMNLq3elYGBfG5XRJJ13H8BMvfUAHNzTwsiBZjbknmVj9YccXXE7/U1rEEhqRk4yfLyR/PBq7qafirWHoPk1fMfBDAKMIMQIQswgwKr6GFJSqMsxsLSd1Gie5mM9hKZJ7+rl9JVq+W7HvyLr5/nw/n8ku8bk5L2bkY6NHxgUSgnKVRtDKFLJKtlU9TzHnsmUTWXUZ82X60iMmPR+NKBgFM9sr9/p0Phikp4PFem/vsiVjBQhPmWqlCgrn1EC8kpSIqQsJT4+EgVIDAIEwXlLC4HAj3NZBCQISBOQISBDSIaANBLnvO93gCSQFJHIMs/6E5hE2u/MJRLn61RB9EoVKAhQ+IAPFBTkeasb2QDaDYtO06ZJGORkSKj8SUMeJVM25ZKPUoo+YXFAKU7IgBRwnSHYaggSsySelJIcVkfYE+7Gp8pGcxPbzGvPE0/n7SeHCf1jhP5RgsoRopILDHspVmINpr0U016GYbVPaTD5XD0Ppkpz8zzNllmkLAhLk+u6HwO+SPSS8yee5/3lOdu3Al8FcsBTwK95njc3HfuzgFIKJceQwSnCoJvQP07ov4n0jxMJIgMrsZJE7r1Y1nIwsigl4wCdsyuYfOVzWp3iWHiMQ/IgA6ofgWC1s5YuVlOnapEyIFQ+byjFi3tr6ZSCTSsMQumzbMcrhIkEr9VfgxOGZMgznSGrRgjLjpoUmgWj09pz4SIE1K3o59RrSxnc2UnTTUcRAsaO1TFyoJmmrgKV9hZGDiyh6+jTdB19+sy+oWFxuPYmjo/cQEewkaOVBhrTL+AnE4SWiTQtpGngOwl6V3Qw0lh7pomkR0ZZsfsgbYfeJNnUwH3iabZnbuarmz9DS+E02UeL9De0MlpNce4j17ED2ptG6WwZprUhj2FEZoiOH2RJnbQY/Ig6SzABDG2ukO6xaf9+imKrT3HplXv9DGXikMEhQw3QDCDeEg1CGEhCAqr4qkwgqigii1BIQKgCJAECAQgERlTDQmDhYJHAFjYplQThUFSRLatCJGyqKCoqWoZKEcZpgVKEROtnRrKp8eC9k2MLgYWBJQQ2ghbDYIUQsbUr6oZEBYQyGjw4lRurEIIWQloEDJkWnoKnZcDLUrHVEGwyBLkZFk9CGKwSq+gUnexX+3kj3Mvu8HWWGB2sM9azwuii1jg/ELkw6rCcOixnC4lMgApPEQYnkH431eJTcGZQvYlhLcG0l2HanRhWC4bZiGE2aqeamjPMu6XJdd0O4GngOqJ7xrPARz3P2zshz27glz3Pe9513b8BXvY878tT/IoVzIGlSSkFqopSBZQsomS8VEVUOIaUw8hwCBUOEAa9oN5ymiiMLKbdgWUvxbDbMcwWlIr7EGaBUIUUKVBQ0d+wGmZQDdAn++hTvUgkAsESs5Ol5gqWGEvIpZLk8yWKSnFUKV4JFf2+YMNfrSMtLTru7yHdfYwNP/kR37/hI7zobMbNjtHI6amXQsGmnTbLj1ocu8PkhFw41orLsTSNUziVY/BQE5m2PEaqytiRempaKrSt76UcBKAkufxpssU+BIKRmlYKqUaUMKFgs+V1CzNUvFSbYeSOfqxlY1ML8zKeR0EgDQbzrYwMOhiDBXKVUepqQ6yOLCJtIhWU84LBUpruYi1VaZIwfFbZvbxrX4n2QykK20oc3xIwLBPkA5tqYGAGPmbg4xRCbng2g+XD/lUn6M8USMgqyYykpiUk7MhSac4hbfO8GDVmsYI9VkZaBmHaIUxdxJfS+H3rIhUQhIIgMLGtcMbcLoyLpvlAxBdSTDArifPsS5OjUNHL2mXeUy5W/hEMPAXHZCS7lgrBCgHLDEETzPjYp6qq0k0Ph8ODDKvIml0vGmgTbbQYLTSKJnIiR43IYV3AghTdt0dRYR8yHCAMThP6J1Ayf1Y+YWQj8WQ2kck2U64kEEY2So+XQqRBJBAiAcKes9Av2tI0tywE0fQp4E7P8z4dr/8uIDzP+6/x+nLgcc/zVsXrdwC/53neu6b4FauAg0NDhUsWTY2NWQYGoh9Rvv8vCP1ulAqAIFrGny+GEEkMsw7Dqo3fXuoxzFqEWQ9kiATS9M9vVI7wiP9DAhViChMTA4NoGb33BoQqJCQgIKCkSpQ5v8srLdLUiWYKYiUmWWwcJIJQKapA2RAMBJKRONmXpGoAAAzhSURBVH/nzgaat7cRnnLYtPF5ehIhYSnkWF0Xx7NL6UwVWWn34supGQSbeg027rLJ5g3615kcaa3MuSfwizETokkpKJ6uZfhYPSoUNCwvUr9ikIo/tYdwogTr96ZIlyVD2AxbJpVcgHIk2BKjtYiwJIioJZ11J51w6iL+bPohzcdPkRkawVDn7iEIEYw4LZTMVhoLSZKh4skVWX68KndRsVJfCvj4zgHa8gGnMxYnamxeWJqluzZBXXmI+vIQlgowRYgyDHxhE2DiCxvfsKmaCRQCixDTlBgmSBEVygwCMtUCmdIY6WqBpKoQJBOU02kqiRR5M0ueNHnSlCd0e5mEJPBxRBWL8MxfgiopUSGpKpiEcQ2o+HoJ/MAgCEz80KSqLIQhsIwAy5DYIsAJKyTLRbae2kF9fhAhFUHGIcgkCbIOQTZJkEkirfEHqDhrEX1+61sFgFQIKRFSYRYqOEN5EsMF7JESKIkyTap1afy6DJX6LGEqQZi0Gdq2AunM7ow/J2lTKV+8vZYw6MbgWBgyGDu1FUCdgCyQAJJCkADqBWwxxGWFu1FKUaTMaXrpk6cZkP0U1Fuix8LiE84naTAapng8gDJKjqDkGErmUeEoUg4TBkPRupxKpAArFlCJWECZCCNFpvFXMa3WSynqpDQ2ZruAbqZmJNRcJguhe24JcHLC+kngxrfZ3jmN47cD1NdnLvX8gEg4RcvfIQwLoEJQ4+9tKrqZAZHRPFqfOKPtrXyXJo4uRD2t/CrrqMRdAW+927xlXlDxfzU+CkOFSBShCpEqfGu7kggMzAmm6PFtoYr29oGqlHCvwrrHoZoX+IWbWYrAwkQGCl+CH9QQyGnU+Vrg7gTlsiJblWwI5nbq+twiMA2QKodSNdPaU90CFdskm7GoSYGZEggLMKAqAwI5XaF5DWWIZyPFFozx5qkkKQUpCQQQVuC2quQ2JcGImrKc9DJZcGcLoWnRmISGhMF6U1FSFWwrh2XmMER0zgKBUpFAkUohpULFgZKFITCEEYnAWDQBGChMIRFCRfnic5cIFBCGkUVNKpDxsQwMDMNGiNSZszREvE2AYcTfeaaio38y/snKEFQ86UIgMAxxZl8AX95AOawwLn2EAEuY0Q1WnPk39QHyE7viFQRK4k+4dwgEpjBITzjpurmKs5d6+yyb4qUhTAwEloi6B6OmGomkaEyWIJClM3V7OayKlwIQwjzTKWoKkxoji5iRSTPijMg928onQJhn2uJ46Tizzpn9DDM701aoI0AXcHQmD6qZnIUgms7thxofvzjV7W/HS8AdRGJrRkwXpnl5Amw2mLce9/OHEGg0Go1mbume7xNYLCwE0dRNJGrGaQNOnLO9/SLb344K0ZgpjUaj0Wg0mktmbkaqXZyfAHe7rtvsum4a+BDw6PhGz/OOAWXXdW+Lk34ReGTuT1Oj0Wg0Gs1iZt5Fk+d5PcAXgCeA14BveJ73ouu6P3Rd9/o428eBP3Zddx/RWMI/m5+z1Wg0Go1Gs1iZ99lzGo1Go9FoNFcC825p0mg0Go1Go7kS0KJJo9FoNBqNZgpo0aTRaDQajUYzBbRo0mg0Go1Go5kCWjRpNBqNRqPRTIGF4Nxyzonj3f1P4HSc9LDneV9wXXcZ8CDQAnjAxz3Py7uuWwd8HVgJ9AEf8TzvlOu6CeBvgOuBEvAxz/P2ua4rgD8E3kfkvfwznuc9M4dFnDFc1/0Y8EXABv7E87y/nOdTuixc132C6PqOB9D6VaIIDOeV0XXde4AvEQWO+LbneV+M07cCXwVywFPAr3meF1yo/cxV2d4O13VzRAGx3+d53tGZKt90fx9zWOTzmKQO/g9wO1CIs/ye53nfm+26mavyTsR13f8MfCRefdjzvM8vtjZwgTpYNG1Ac/ksVkvT9cC/9zxva/z3hTj9r4C/8jxvHfAy8Ltx+u8D2z3PWw/8NfCncfpvAIU4/TeBr8XpHwLWAxuADwBfc133ihOorut2AP+d6IayFfgV13U3zO9ZXTqxmF0LbBm/9kQe588ro+u6KeBvgfcTXcsbXNd9T3yoB4HPep63liisz2fi9Au1n3nHdd2biDzjr43XZ7J80/19zAvn1kHM9UQBw8fvBd+bo7qZU2IBcB+wjaidX+e67kdZRG3gAnXwQRZJG9DMDItVNN0AfMp13ddd133Qdd1613Vt4E7gH+M8XwPujz+/l+hNAeCbwHvi/GfSPc97CmiO3zbeC3zL8zzped5+4E3g1jko10xzD/C453mDnucViOrmw/N8TpeDGy9/5LruTtd1P8uFy3gjcMDzvCOe5wVEN8r7XdddDqQ8z3s+PtbX4vSLtZ+FwGeAX+etEEQzWb7p/j7mi7PqII5AsAz4W9d1d7mu+3uu6xrMTd3MNSeB3/Y8r+p5ng+8QSQeF1MbmKwOlrF42oBmBlisoukk8N+AzcBx4C+AJmA0/oGM5+mMPy+J14m3jwLNE9PP2edC6VcaV0s5xqkHHgM+CNwN/BrRDXM61/BC6RdrP/OO53m/7Hne9glJM1m+6f4+5oVJ6qANeBz4N8DNRDEwP83c1M2c4nnenvEHveu6a4i6qCSLqA1coA4eZZG0Ac3McMV1GU0H13XvB/74nOR9nufdMyHP/wIOAZ8HznWPLuOlOCddxNuMc/Z5u/QrjaulHAB4nvcc8Nz4uuu6f0M0ZuH3J2S71Gt7bjos7LqayfJN9/exIPA87zCRgAbAdd0/Bz5JZC2Y7bqZF1zX3Qg8DPwHIODsrspF0QYm1oHneR6LrA1oLo+r2tLked4/eJ7XOfEP+JDrur81IZsgunn0ArWu65pxejtvdWX0EL2VEo9NqgEGiMbDtE84Vlu8z4XSrzSulnIA4Lru7a7r3j0hSQBHmd41vFD6xdrPQmQmyzfd38eCwHXda1zX/dCEJEE0QWAu6mbOiYOePwb8jud5f8cibAPn1sFiawOay+eqFk0XIA98Ph4UCvBZ4HtxH/d24Bfi9E8Cj8SffxivE2/fHuc/k+667u1A2fO8N+P0j7uua7quu5robe6l2S3WrPAT4G7XdZvj8R8fIjJnX6nUAX/oum7Sdd0a4FPAJ5i8jC8Aruu6q+Mb4ceARzzPOwaU45svwC/G6RdrPwuRmSzfdH8fCwUB/MmEMY2/AnyPuambOcV13aXAPxHNXvtWnLyo2sAF6mDRtAHNzHBVd89Nhud5oeu6HwG+HM+Q2M9bDfrfAX/nuu4XiQZvfzRO/12iGXB7gGHg43H6nwP/O06vEP2AIDLt3gTsitc/7XleaRaLNSt4ntfjuu4XgCeABPBVz/NenOfTumQ8z3soFss7ABP4S8/znrlQGV3XfQD4DpAkuvGND/L8OPDXbjR9/VXgz+L0C7WfBYfneeUZLN90fx8LAs/zdrmu+z+AZ4jcTXzH87xvwoxe+wvVzVzzOaKyfMl1x+dD8BXgARZPG7hQHSyWNqCZAYRS53bDajQajUaj0WjOZTF2z2k0Go1Go9FMGy2aNBqNRqPRaKaAFk0ajUaj0Wg0U0CLJo1Go9FoNJopoEWTRqPRaDQazRRYdC4HNBrNhXFddwVwBHjK87x3nLPta0S+rZqB1URTtRuJXr6OA5/zPG9PnFcBu4GQyFNymih8xL/1PO/leDr3n8bfpYj85RTiYzyHRqPRLEC0aNJoNOdSJnLstzx25ofruhlg3KGfCTwE3Od53qvx9k8Aj7iu2+V5Xhjne6fnef3jB3Vd93NEfntuiZO2e573vgnbfw74ruu6SyfE8NJoNJoFg+6e02g05xIC3+ZsJ3w/D/xz/FkSeVfPTtj+dSLv+iaTEIePWAYMXuR7HyMKN1F3SWet0Wg0s4y2NGk0msn4e+BB4A/i9U8Bvwn8NlF32ueBR13XPUXkTfkJ4Fue51UnHOOJuJuuich69RDwS5N9meu6giiExe6J1imNRqNZSGhLk0ajOQ/P814BQtd1r4tjdtV4nrd7wvYvAa3AbwAngf8I7HBdt3bCYd7ped5m4H1EY5qe8Dyvd8L2O1zXfc113R3AHuBOoth/Go1GsyDRliaNRnMh/i9RQOO++PM4G13XvdHzvD8ksh495LrufyIa+H0vb8XoAsDzvFdd1/0tovhbOzzPOxpvOmtMk0aj0Sx0tKVJo9FciAeB+4kis39jQnoJ+GIctX6cdqAWeH2yA8VBUF8E/nh2TlWj0WhmH21p0mg0k+J5Xo/rum8AI57nTRzAfRj4APAHrut2Eo1XGgF+yfM87yKH/Cywy3Xdd8/aSWs0Gs0sIpRS830OGo1Go9FoNAse3T2n0Wg0Go1GMwW0aNJoNBqNRqOZAlo0aTQajUaj0UwBLZo0Go1Go9FopoAWTRqNRqPRaDRTQIsmjUaj0Wg0mimgRZNGo9FoNBrNFPj/qiW52Nh/xVgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 589.1x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(temp_df, diag_kind='kde', palette=sns.color_palette(\"hls\", 7), hue='Country', height=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Fuel economy city`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EPA Fuel Economy Est - City (MPG)'] = df['EPA Fuel Economy Est - City (MPG)'].str.split().str.get(0).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a35002a0f0>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['EPA Fuel Economy Est - City (MPG)'].dropna(), color='deeppink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    185\n",
       "0.0      1\n",
       "Name: Hybrid Engine, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['EPA Fuel Economy Est - City (MPG)'] > 45]['Hybrid Engine'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Fuel system`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gasoline Direct Injection    5547\n",
       "Sequential MPI               5085\n",
       "Electronic Fuel Injection    3701\n",
       "Direct Injection             2655\n",
       "SFI                          2610\n",
       "SMPI                         2345\n",
       "MPFI                         2012\n",
       "EFI                          1810\n",
       "SEFI                         1075\n",
       "MPI                           824\n",
       "Name: Fuel System, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel System'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fuel System'] = df['Fuel System'].str.replace('Electronic Fuel Injection', 'EFI')\n",
    "df['Fuel System'] = df['Fuel System'].str.replace('Sequential MPI', 'SMPI')\n",
    "df['Fuel System'] = df['Fuel System'].str.replace('Electronic fuel injection', 'EFI')\n",
    "df['Fuel System'] = df['Fuel System'].str.replace('SEFI', 'SEFI')\n",
    "df['Fuel System'] = df['Fuel System'].str.replace('MFI', 'MPI')\n",
    "df['Fuel System'] = df['Fuel System'].str.replace('MPFI', 'MPI')\n",
    "df['Fuel System'] = df['Fuel System'].str.replace('Electronic Fuel Injectino', 'EFI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Third gear ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Third Gear Ratio (:1)'] = df['Third Gear Ratio (:1)'].str.replace('-TBD-', '').replace(r'^\\s*$', \n",
    "                                                            np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Third Gear Ratio (:1)'] = df['Third Gear Ratio (:1)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Murano          119\n",
       "Altima          104\n",
       "Rogue            98\n",
       "Legacy           89\n",
       "Outback          84\n",
       "Prius            84\n",
       "Pathfinder       76\n",
       "Accord Sedan     74\n",
       "Sentra           72\n",
       "A4               62\n",
       "Titan            60\n",
       "Maxima           58\n",
       "Model S          53\n",
       "Civic Hybrid     52\n",
       "CR-V             50\n",
       "Name: Model, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Third Gear Ratio (:1)'].isnull()]['Model'].value_counts().head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `First gear ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['First Gear Ratio (:1)'] = df['First Gear Ratio (:1)'].str.split().str.get(0)\n",
    "df['First Gear Ratio (:1)'] = df['First Gear Ratio (:1)'].str.split('-').str.get(0)\n",
    "df['First Gear Ratio (:1)'] = df['First Gear Ratio (:1)'].str.strip('TBD') # not sure here\n",
    "df['First Gear Ratio (:1)'] = df['First Gear Ratio (:1)'].str.replace('-TBD-', '').replace(r'^\\s*$', \n",
    "                                                            np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1872"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['First Gear Ratio (:1)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3501fbdd8>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['First Gear Ratio (:1)'].dropna(), color='blueviolet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Fourth gear ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fourth Gear Ratio (:1)'] = df['Fourth Gear Ratio (:1)'].str.split('-').str.get(0).str.split('/').str.get(0)\n",
    "df['Fourth Gear Ratio (:1)'] = df['Fourth Gear Ratio (:1)'].str.split().str.get(0).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3501cf3c8>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Fourth Gear Ratio (:1)'].dropna(), color='mediumvioletred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fourth Gear Ratio (:1)'].clip(upper=2.5, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Second gear ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3296"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Second Gear Ratio (:1)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Second Gear Ratio (:1)'] = df['Second Gear Ratio (:1)'].str.split('-').str.get(0)\n",
    "df['Second Gear Ratio (:1)'] = df['Second Gear Ratio (:1)'].str.split().str.get(0).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34ffd2748>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Second Gear Ratio (:1)'].dropna(), color='sienna')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019 Mercedes-Benz A Class A 220 4MATIC Sedan    10.04\n",
       "2019 Mercedes-Benz A Class A 220 Sedan           10.04\n",
       "2005 MINI Cooper 2-Door Coupe                     8.33\n",
       "2005 MINI Cooper 2-Door Coupe S                   7.79\n",
       "2002 MINI Cooper 2-Door Coupe S                   7.18\n",
       "Name: Second Gear Ratio (:1), dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Second Gear Ratio (:1)'] > 4]['Second Gear Ratio (:1)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Steering Type`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Steering Type'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rack Pinion                                      11457\n",
       "Power Rack Pinion                                11449\n",
       "Power                                             4626\n",
       "Electric Rack Pinion                               937\n",
       "Recirculating Ball                                 747\n",
       "Power Recirculating Ball                           653\n",
       "Variable Assist Power                              239\n",
       "Variable Assist Power Rack Pinion                  217\n",
       "Electric Power                                     174\n",
       "Power Speed Sensitive                              153\n",
       "Electric Power Rack Pinion                         144\n",
       "Variable Assist Power Assist                        89\n",
       "Servotronic Speed Sensitive                         77\n",
       "Power Speed Sensitive Rack Pinion                   77\n",
       "Speed Sensitive                                     76\n",
       "Electric Variable Power Rack Pinion                 76\n",
       "Servotronic                                         55\n",
       "Hydraulic Recirculating Ball                        42\n",
       "Speed Sensitive Rack Pinion                         39\n",
       "Variable Rate Power Rack Pinion                     38\n",
       "Electric                                            38\n",
       "Variable Power Rack Pinion                          32\n",
       "Power Speed Sensitive Recirculating Ball            29\n",
       "Electromechanical                                   26\n",
       "Servotronic Variable Assist Power                   25\n",
       "Variable Effort Power                               22\n",
       "Power Ball And Nut                                  16\n",
       "Power Variable                                      14\n",
       "Power Electric Assist                               14\n",
       "Power Worm & Roller                                 13\n",
       "Hydraulic Power Rack Pinion                         13\n",
       "Performance Rack Pinion                             12\n",
       "Quick Ratio Power Rack Pinion                       11\n",
       "Hydraulic Assist Power                               9\n",
       "Variable Assist Power Recirculating Ball             8\n",
       "Electric Variable Effort Power                       7\n",
       "Performance Tune                                     6\n",
       "Power Rack Pinion W/Electric Variable Assist         5\n",
       "Performance                                          5\n",
       "Hydraulic Power                                      3\n",
       "M Servotronic Variable Assist Power                  2\n",
       "Power Rack Pinion W/Hydraulic Variable Assist        2\n",
       "Servotronic Power Assist                             2\n",
       "4 Wheel Power Rack Pinion                            1\n",
       "Name: Steering Type, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Steering Type'] = df['Steering Type'].str.title().str.replace('Pwr', 'Power').str.replace('Rack & Pinion', 'Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Speed Sensitive Power', 'Power Speed Sensitive')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Speed-Sensitive Power', 'Power Speed Sensitive')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Power Rack-Pinion Steering', 'Power Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.strip('- Tbd -').replace(r'^\\s*$', np.nan, regex=True)\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Power Rack-&-Pinion', 'Power Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Power Rack-And-Pinion', 'Power Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Electric-Assist Rack-Pinion', 'Electric Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Rack-Pinion Steering', 'Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Electric Rack-&-Pinion', 'Electric Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Vehicle-Speed-Sensitive', 'Speed-Sensitive')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Vehicle Speed-Sensitive', 'Speed-Sensitive')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Electric Power Steering', 'Electric Power')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Power, Speed-Sensitive', 'Power Speed Sensitive')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Power Speed-Sensitive', 'Power Speed Sensitive')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Hyrdaulic', 'Hydraulic')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Vehicle-Power Speed Sensitive Rack-Pinion', \n",
    "                                                      'Power Speed Sensitive Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Power Rack And Pinion', 'Power Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Variable Power', 'Variable-Assist Power')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Var Power Rack-Pinion', 'Variable Power Rack-Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('-', ' ')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Re Circulating Ball', 'Recirculating Ball')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Recir ', 'Recirculating ') \n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Electric Power Assisted Rack Pinion', 'Electric Power Rack Pinion')\n",
    "df['Steering Type'] = df['Steering Type'].str.replace('Power Assiste', 'Power')\n",
    "df['Steering Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34ff08470>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Steering Type'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Rear wheel material`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aluminum           21190\n",
       "Steel               6415\n",
       "Alloy               2659\n",
       "Styled Steel         636\n",
       "Forged Aluminum      561\n",
       "Chrome               256\n",
       "Carbon Fibre           4\n",
       "Name: Rear Wheel Material, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rear Wheel Material'] = df['Rear Wheel Material'].str.strip('- TBD -').replace(r'^\\s*$', np.nan, regex=True)\n",
    "df['Rear Wheel Material'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front wheel material`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aluminum           21190\n",
       "Steel               6415\n",
       "Alloy               2659\n",
       "Styled Steel         636\n",
       "Forged Aluminum      561\n",
       "Chrome               256\n",
       "Carbon Fibre           4\n",
       "Name: Front Wheel Material, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Front Wheel Material'] = df['Front Wheel Material'].str.strip('- TBD -').replace(r'^\\s*$', np.nan, regex=True)\n",
    "df['Front Wheel Material'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front suspension type`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Suspension Type - Front'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.title().str.replace('- TBD -', '').replace(r'^\\s*$', \n",
    "                                                                                                       np.nan, regex=True)\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Macpherson Strut', 'Macpherson')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Coil Spring', 'Coil')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('-', ' ')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('4', 'Four')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('5', 'Five')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Macpherson Type', 'Macpherson')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Short/Long Arm', 'Short And Long Arm')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Strut Type', 'Strut')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Four Wheel Independent', 'Independent')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Double Whishbone', 'Double Wishbone')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Four Link', 'Multi Link')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Short Long Arm', 'Short And Long Arm')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Independent Four Link', 'Short And Long Arm')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Five Link', 'Multi Link')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Independent 3 Link', 'Multi Link')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Independent Multi Link', 'Multi Link')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Independent Multi Link', 'Multi Link')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('W/', '').str.replace(',', '').str.strip()\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Indep ', 'Independent ')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Ind ', 'Independent ')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Macphersons', 'Macpherson')\n",
    "df['Suspension Type - Front'] = df['Suspension Type - Front'].str.replace('Torsion Bars', 'Torsion Bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Suspension Type - Rear', 'Front Wheel Material'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Front brake rotor diameter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2384"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Front Brake Rotor Diam x Thickness (in)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Front Brake Rotor Diam x Thickness (in)'] = df['Front Brake Rotor Diam x Thickness (in)'].str.split().str.get(0)\n",
    "df['Front Brake Rotor Diam x Thickness (in)'] = df['Front Brake Rotor Diam x Thickness (in)'].str.strip('\"').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicolas\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34d9e5518>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Front Brake Rotor Diam x Thickness (in)'].dropna(), color='m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rear brake rotor diameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5308"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rear Brake Rotor Diam x Thickness (in)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rear Brake Rotor Diam x Thickness (in)'] = df['Rear Brake Rotor Diam x Thickness (in)'].str.split().str.get(0)\n",
    "df['Rear Brake Rotor Diam x Thickness (in)'] = df['Rear Brake Rotor Diam x Thickness (in)'].str.strip('\"')\n",
    "df['Rear Brake Rotor Diam x Thickness (in)'] = df['Rear Brake Rotor Diam x Thickness (in)'].str.strip('-TBD')\n",
    "df['Rear Brake Rotor Diam x Thickness (in)'] = df['Rear Brake Rotor Diam x Thickness (in)'].replace(r'^\\s*$', \n",
    "                                                                            np.nan, regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yes/no features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_binary = ['Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear', 'Air Bag-Side Head-Front', \n",
    "       'Air Bag-Side Head-Rear', 'Brakes-ABS', 'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars', 'Fog Lamps', \n",
    "       'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera', 'Stability Control']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"for spec in specs_binary:\\n    df[spec] = df[spec].replace('Yes', '1').replace('No', '0')\\n    df[spec] = df[spec].astype(float)\""
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''for spec in specs_binary:\n",
    "    df[spec] = df[spec].replace('Yes', '1').replace('No', '0')\n",
    "    df[spec] = df[spec].astype(float)'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Warranty "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_warranty = ['Basic Miles/km', 'Basic Years', 'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
    "                  'Drivetrain Years', 'Roadside Assistance Miles/km', 'Roadside Assistance Years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36000.0     20484\n",
       "50000.0      7731\n",
       "60000.0      2585\n",
       "150000.0      332\n",
       "100000.0      295\n",
       "72000.0       162\n",
       "24000.0       145\n",
       "Name: Basic Miles/km, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Basic Miles/km'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    20789\n",
       "4     7985\n",
       "5     2638\n",
       "6      167\n",
       "2      155\n",
       "Name: Basic Years, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Basic Years'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150000    22233\n",
       "100000     7785\n",
       "50000      1233\n",
       "60000         6\n",
       "Name: Corrosion Miles/km, dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Corrosion Miles/km'] = df['Corrosion Miles/km'].str.replace(',', '').str.replace('Unlimited', '150000')\n",
    "df['Corrosion Miles/km'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     16162\n",
       "6      7181\n",
       "12     4767\n",
       "7      1480\n",
       "4      1097\n",
       "3       258\n",
       "10      239\n",
       "8        63\n",
       "2        10\n",
       "Name: Corrosion Years, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Corrosion Years'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000.0     14060\n",
       "100000.0     6583\n",
       "50000.0      5521\n",
       "36000.0      2912\n",
       "70000.0      1862\n",
       "150000.0      476\n",
       "72000.0       162\n",
       "24000.0        15\n",
       "120000.0        7\n",
       "125000.0        1\n",
       "Name: Drivetrain Miles/km, dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drivetrain Miles/km'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     18479\n",
       "4      5529\n",
       "3      3198\n",
       "10     2138\n",
       "6      1939\n",
       "7       105\n",
       "12      100\n",
       "8        81\n",
       "2        30\n",
       "Name: Drivetrain Years, dtype: int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drivetrain Years'] = df['Drivetrain Years'].str.replace('Unlimited', '12')\n",
    "df['Drivetrain Years'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000.0    10402\n",
       "36000.0      7506\n",
       "60000.0      5783\n",
       "50000.0      2681\n",
       "70000.0       767\n",
       "25000.0       401\n",
       "24000.0         3\n",
       "Name: Roadside Assistance Miles/km, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Roadside Assistance Miles/km'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    10701\n",
       "3     7737\n",
       "4     6285\n",
       "2     1425\n",
       "6      767\n",
       "8      631\n",
       "Name: Roadside Assistance Years, dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Roadside Assistance Years'] = df['Roadside Assistance Years'].str.replace('Unlimited', '8')\n",
    "df['Roadside Assistance Years'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "for spec in specs_warranty:\n",
    "    df[spec] = df[spec].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AFTER IMPUTATION!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSRP', 'Make', 'Model', 'Style Name', 'Drivetrain',\n",
       "       'Passenger Capacity', 'Passenger Doors', 'Body Style',\n",
       "       'EPA Classification', 'Front Shoulder Room (in)',\n",
       "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
       "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
       "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
       "       'Fuel Tank Capacity, Approx (gal)', 'EPA Fuel Economy Est - Hwy (MPG)',\n",
       "       'EPA Fuel Economy Est - City (MPG)', 'Fuel System',\n",
       "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
       "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
       "       'Front Brake Rotor Diam x Thickness (in)',\n",
       "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
       "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
       "       'Suspension Type - Front', 'Air Bag-Frontal-Driver',\n",
       "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
       "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
       "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
       "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
       "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
       "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
       "       'Stability Control', 'Other Features', 'Basic Miles/km', 'Basic Years',\n",
       "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
       "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
       "       'Roadside Assistance Years', 'Hybrid', 'Hybrid Engine', 'Gears',\n",
       "       'Net Horsepower', 'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM',\n",
       "       'Cylinders', 'Engine Configuration', 'Displacement (L)',\n",
       "       'Displacement (cc)', 'Rear Tire Width', 'Front Tire Width',\n",
       "       'Rear Wheel Size', 'Front Wheel Size', 'Tire Rating',\n",
       "       'Tire Width Ratio', 'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country',\n",
       "       'Country Code', 'Category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Hybrid', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"specs_to_dummies = ['Drivetrain', 'Body Style', 'EPA Classification', 'Fuel System', 'Steering Type',  'Rear Wheel Material',  \\n                    'Suspension Type - Front', 'Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', \\n                    'Air Bag-Passenger Switch (On/Off)', 'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\\n                    'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS', \\n                    'Child Safety Rear Door Locks', 'Daytime Running Lights', 'Traction Control', \\n                    'Night Vision', 'Rollover Protection Bars', 'Fog Lamps', 'Parking Aid', \\n                    'Tire Pressure Monitor', 'Back-Up Camera', 'Stability Control', 'Hybrid Engine', \\n                    'Engine Configuration', 'Tire Rating', 'Country', 'Category']\""
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''specs_to_dummies = ['Drivetrain', 'Body Style', 'EPA Classification', 'Fuel System', 'Steering Type',  'Rear Wheel Material',  \n",
    "                    'Suspension Type - Front', 'Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', \n",
    "                    'Air Bag-Passenger Switch (On/Off)', 'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
    "                    'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS', \n",
    "                    'Child Safety Rear Door Locks', 'Daytime Running Lights', 'Traction Control', \n",
    "                    'Night Vision', 'Rollover Protection Bars', 'Fog Lamps', 'Parking Aid', \n",
    "                    'Tire Pressure Monitor', 'Back-Up Camera', 'Stability Control', 'Hybrid Engine', \n",
    "                    'Engine Configuration', 'Tire Rating', 'Country', 'Category']'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"for item in specs_to_dummies:\\n    dummies = pd.get_dummies(df[item], prefix_sep=': ', prefix=item)\\n    df = pd.concat([df, dummies], sort=False, axis=1)\\n\\n#df = df.drop(specs_to_dummies, axis=1)\\ndf = df.drop('Other Features', axis=1)\""
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''for item in specs_to_dummies:\n",
    "    dummies = pd.get_dummies(df[item], prefix_sep=': ', prefix=item)\n",
    "    df = pd.concat([df, dummies], sort=False, axis=1)\n",
    "\n",
    "#df = df.drop(specs_to_dummies, axis=1)\n",
    "df = df.drop('Other Features', axis=1)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31738, 81)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 31738 entries, 2017 Acura RDX FWD w/Technology/AcuraWatch Plus Pkg to 2015 Volvo V60 Cross Country 2015.5 4-Door Wagon T5 Platinum AWD\n",
      "Data columns (total 81 columns):\n",
      "MSRP                                       31668 non-null float64\n",
      "Make                                       31735 non-null object\n",
      "Model                                      31735 non-null object\n",
      "Style Name                                 31735 non-null object\n",
      "Drivetrain                                 31735 non-null object\n",
      "Passenger Capacity                         31735 non-null float64\n",
      "Passenger Doors                            31735 non-null float64\n",
      "Body Style                                 31733 non-null object\n",
      "EPA Classification                         20748 non-null object\n",
      "Front Shoulder Room (in)                   30403 non-null float64\n",
      "Front Head Room (in)                       30447 non-null float64\n",
      "Second Leg Room (in)                       27187 non-null float64\n",
      "Front Leg Room (in)                        30440 non-null float64\n",
      "Second Shoulder Room (in)                  27123 non-null float64\n",
      "Second Head Room (in)                      27207 non-null float64\n",
      "Height, Overall (in)                       31527 non-null float64\n",
      "Wheelbase (in)                             31685 non-null float64\n",
      "Width, Max w/o mirrors (in)                31587 non-null float64\n",
      "Fuel Tank Capacity, Approx (gal)           31487 non-null float64\n",
      "EPA Fuel Economy Est - Hwy (MPG)           27990 non-null float64\n",
      "EPA Fuel Economy Est - City (MPG)          27994 non-null float64\n",
      "Fuel System                                30829 non-null object\n",
      "Third Gear Ratio (:1)                      28437 non-null float64\n",
      "First Gear Ratio (:1)                      29866 non-null float64\n",
      "Fourth Gear Ratio (:1)                     28430 non-null float64\n",
      "Second Gear Ratio (:1)                     28442 non-null float64\n",
      "Front Brake Rotor Diam x Thickness (in)    29354 non-null float64\n",
      "Rear Brake Rotor Diam x Thickness (in)     26418 non-null float64\n",
      "Steering Type                              31680 non-null object\n",
      "Turning Diameter - Curb to Curb            30159 non-null float64\n",
      "Rear Wheel Material                        31721 non-null object\n",
      "Suspension Type - Front                    31563 non-null object\n",
      "Air Bag-Frontal-Driver                     31735 non-null object\n",
      "Air Bag-Frontal-Passenger                  31735 non-null object\n",
      "Air Bag-Passenger Switch (On/Off)          31735 non-null object\n",
      "Air Bag-Side Body-Front                    31735 non-null object\n",
      "Air Bag-Side Body-Rear                     31735 non-null object\n",
      "Air Bag-Side Head-Front                    31735 non-null object\n",
      "Air Bag-Side Head-Rear                     31735 non-null object\n",
      "Brakes-ABS                                 31735 non-null object\n",
      "Child Safety Rear Door Locks               31735 non-null object\n",
      "Daytime Running Lights                     31735 non-null object\n",
      "Traction Control                           31735 non-null object\n",
      "Night Vision                               31735 non-null object\n",
      "Rollover Protection Bars                   31735 non-null object\n",
      "Fog Lamps                                  31735 non-null object\n",
      "Parking Aid                                31735 non-null object\n",
      "Tire Pressure Monitor                      31735 non-null object\n",
      "Back-Up Camera                             31735 non-null object\n",
      "Stability Control                          31735 non-null object\n",
      "Other Features                             31735 non-null object\n",
      "Basic Miles/km                             31734 non-null float64\n",
      "Basic Years                                31734 non-null float64\n",
      "Corrosion Miles/km                         31257 non-null float64\n",
      "Corrosion Years                            31257 non-null float64\n",
      "Drivetrain Miles/km                        31599 non-null float64\n",
      "Drivetrain Years                           31599 non-null float64\n",
      "Roadside Assistance Miles/km               27543 non-null float64\n",
      "Roadside Assistance Years                  27546 non-null float64\n",
      "Hybrid Engine                              31735 non-null float64\n",
      "Gears                                      28970 non-null float64\n",
      "Net Horsepower                             31694 non-null float64\n",
      "Net Horsepower RPM                         31401 non-null float64\n",
      "Net Torque                                 31633 non-null float64\n",
      "Net Torque RPM                             31275 non-null float64\n",
      "Cylinders                                  31470 non-null float64\n",
      "Engine Configuration                       30792 non-null object\n",
      "Displacement (L)                           31511 non-null float64\n",
      "Displacement (cc)                          30538 non-null float64\n",
      "Rear Tire Width                            31698 non-null float64\n",
      "Front Tire Width                           31699 non-null float64\n",
      "Rear Wheel Size                            28454 non-null float64\n",
      "Front Wheel Size                           28627 non-null float64\n",
      "Tire Rating                                31684 non-null object\n",
      "Tire Width Ratio                           31695 non-null float64\n",
      "Wheel Size Ratio                           28454 non-null float64\n",
      "Tire Ratio                                 31692 non-null float64\n",
      "Year                                       31735 non-null float64\n",
      "Country                                    31735 non-null object\n",
      "Country Code                               31735 non-null float64\n",
      "Category                                   31733 non-null object\n",
      "dtypes: float64(48), object(33)\n",
      "memory usage: 21.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(r'C:/Users/Nicolas/Documents/Scraping/thecarconnection/raw_data_cleaning_all_may.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.loc[df.isnull().sum(axis=1).sort_values(ascending=False)[:60].index[:30]].isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34da16240>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Hybrid Engine'], df['EPA Fuel Economy Est - Hwy (MPG)'], hue=df['Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4dr Car                                              9178\n",
       "Sport Utility                                        8086\n",
       "2dr Car                                              2513\n",
       "Crew Cab Pickup - Standard Bed - Short Bed           1672\n",
       "Convertible                                          1480\n",
       "Crew Cab Pickup - Standard Bed - Standard Bed        1371\n",
       "Extended Cab Pickup - Standard Bed - Short Bed        974\n",
       "Station Wagon                                         915\n",
       "Extended Cab Pickup - Standard Bed - Standard Bed     866\n",
       "Extended Cab Pickup - Standard Bed - Long Bed         836\n",
       "Regular Cab Pickup - Long Bed                         777\n",
       "Crew Cab Pickup - Standard Bed - Long Bed             746\n",
       "Mini-van, Passenger                                   491\n",
       "Regular Cab Pickup - Short Bed                        400\n",
       "Regular Cab Pickup - Standard Bed                     388\n",
       "Full-size Passenger Van                               371\n",
       "Full-size Cargo Van                                   369\n",
       "Specialty Vehicle                                     132\n",
       "Mini-van, Cargo                                       104\n",
       "3dr Car                                                64\n",
       "Name: Body Style, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Body Style'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015.0    150\n",
       "2017.0    146\n",
       "2018.0    145\n",
       "2016.0    145\n",
       "2014.0    140\n",
       "2019.0    122\n",
       "2012.0    116\n",
       "2013.0    115\n",
       "2011.0     99\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Make'] == 'Ram']['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-c42a15b2c7cf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
